WorldWideScience

Sample records for based image denoising

  1. Wavelet Based Image Denoising Technique

    Directory of Open Access Journals (Sweden)

    Sachin D Ruikar

    2011-03-01

    Full Text Available This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poisson’s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.

  2. Nonlocal Means-Based Denoising for Medical Images

    Directory of Open Access Journals (Sweden)

    Ke Lu

    2012-01-01

    Full Text Available Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

  3. Image denoising based on wavelet cone of influence analysis

    Science.gov (United States)

    Pang, Wei; Li, Yufeng

    2009-11-01

    Donoho et al have proposed a method for denoising by thresholding based on wavelet transform, and indeed, the application of their method to image denoising has been extremely successful. But this method is based on the assumption that the type of noise is only additive Gaussian white noise, which is not efficient to impulse noise. In this paper, a new image denoising algorithm based on wavelet cone of influence (COI) analyzing is proposed, and which can effectively remove the impulse noise and preserve the image edges via undecimated discrete wavelet transform (UDWT). Furthermore, combining with the traditional wavelet thresholding denoising method, it can be also used to restrain more widely type of noise such as Gaussian noise, impulse noise, poisson noise and other mixed noise. Experiment results illustrate the advantages of this method.

  4. A New Algorithm for Total Variation Based Image Denoising

    Institute of Scientific and Technical Information of China (English)

    Yi-ping XU

    2012-01-01

    We propose a new algorithm for the total variation based on image denoising problem.The split Bregman method is used to convert an unconstrained minimization denoising problem to a linear system in the outer iteration.An algebraic multi-grid method is applied to solve the linear system in the inner iteration.Furthermore,Krylov subspace acceleration is adopted to improve convergence in the outer iteration.Numerical experiments demonstrate that this algorithm is efficient even for images with large signal-to-noise ratio.

  5. Region-based image denoising through wavelet and fast discrete curvelet transform

    Science.gov (United States)

    Gu, Yanfeng; Guo, Yan; Liu, Xing; Zhang, Ye

    2008-10-01

    Image denoising always is one of important research topics in the image processing field. In this paper, fast discrete curvelet transform (FDCT) and undecimated wavelet transform (UDWT) are proposed for image denoising. A noisy image is first denoised by FDCT and UDWT separately. The whole image space is then divided into edge region and non-edge regions. After that, wavelet transform is performed on the images denoised by FDCT and UDWT respectively. Finally, the resultant image is fused through using both of edge region wavelet cofficients of the image denoised by FDCT and non-edge region wavelet cofficients of the image denoised by UDWT. The proposed method is validated through numerical experiments conducted on standard test images. The experimental results show that the proposed algorithm outperforms wavelet-based and curvelet-based image denoising methods and preserve linear features well.

  6. Dictionary-based image denoising for dual energy computed tomography

    Science.gov (United States)

    Mechlem, Korbinian; Allner, Sebastian; Mei, Kai; Pfeiffer, Franz; Noël, Peter B.

    2016-03-01

    Compared to conventional computed tomography (CT), dual energy CT allows for improved material decomposition by conducting measurements at two distinct energy spectra. Since radiation exposure is a major concern in clinical CT, there is a need for tools to reduce the noise level in images while preserving diagnostic information. One way to achieve this goal is the application of image-based denoising algorithms after an analytical reconstruction has been performed. We have developed a modified dictionary denoising algorithm for dual energy CT aimed at exploiting the high spatial correlation between between images obtained from different energy spectra. Both the low-and high energy image are partitioned into small patches which are subsequently normalized. Combined patches with improved signal-to-noise ratio are formed by a weighted addition of corresponding normalized patches from both images. Assuming that corresponding low-and high energy image patches are related by a linear transformation, the signal in both patches is added coherently while noise is neglected. Conventional dictionary denoising is then performed on the combined patches. Compared to conventional dictionary denoising and bilateral filtering, our algorithm achieved superior performance in terms of qualitative and quantitative image quality measures. We demonstrate, in simulation studies, that this approach can produce 2d-histograms of the high- and low-energy reconstruction which are characterized by significantly improved material features and separation. Moreover, in comparison to other approaches that attempt denoising without simultaneously using both energy signals, superior similarity to the ground truth can be found with our proposed algorithm.

  7. Wavelet-Based Denoising Attack on Image Watermarking

    Institute of Scientific and Technical Information of China (English)

    XUAN Jian-hui; WANG Li-na; ZHANG Huan-guo

    2005-01-01

    In this paper, we propose wavelet-based denoising attack methods on image watermarking in discrete cosine transform (DCT) or discrete Fourier transform (DFT) domain or discrete wavelet transform (DWT) domain. Wiener filtering based on wavelet transform is performed in approximation subband to remove DCT or DFT domain watermark,and adaptive wavelet soft thresholding is employed to remove the watermark resided in detail subbands of DWT domain.

  8. Image Denoising of Wavelet based Compressed Images Corrupted by Additive White Gaussian Noise

    Directory of Open Access Journals (Sweden)

    Shyam Lal

    2012-08-01

    Full Text Available In this study an efficient algorithm is proposed for removal of additive white Gaussian noise from compressed natural images in wavelet based domain. First, the natural image is compressed by discrete wavelet transform and then proposed hybrid filter is applied for image denoising of compressed images corrupted by Additive White Gaussian Noise (AWGN. The proposed hybrid filter (HMCD is combination of non-linear fourth order partial differential equation and bivariate shrinkage function. The proposed hybrid filter provides better results in term of noise suppression with keeping minimum edge blurring as compared to other existing image denoising techniques for wavelet based compressed images. Simulation and experimental results on benchmark test images demonstrate that the proposed hybrid filter attains competitive image denoising performances as compared with other state-of-the-art image denoising algorithms. It is more effective particularly for the highly corrupted images in wavelet based compressed domain.

  9. Regularized Fractional Power Parameters for Image Denoising Based on Convex Solution of Fractional Heat Equation

    Directory of Open Access Journals (Sweden)

    Hamid A. Jalab

    2014-01-01

    Full Text Available The interest in using fractional mask operators based on fractional calculus operators has grown for image denoising. Denoising is one of the most fundamental image restoration problems in computer vision and image processing. This paper proposes an image denoising algorithm based on convex solution of fractional heat equation with regularized fractional power parameters. The performances of the proposed algorithms were evaluated by computing the PSNR, using different types of images. Experiments according to visual perception and the peak signal to noise ratio values show that the improvements in the denoising process are competent with the standard Gaussian filter and Wiener filter.

  10. An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization

    Directory of Open Access Journals (Sweden)

    Li Guo

    2016-01-01

    Full Text Available Image denoising methods are often based on the minimization of an appropriately defined energy function. Many gradient dependent energy functions, such as Potts model and total variation denoising, regard image as piecewise constant function. In these methods, some important information such as edge sharpness and location is well preserved, but some detailed image feature like texture is often compromised in the process of denoising. For this reason, an image denoising method based on local adaptive regularization is proposed in this paper, which can adaptively adjust denoising degree of noisy image by adding spatial variable fidelity term, so as to better preserve fine scale features of image. Experimental results show that the proposed denoising method can achieve state-of-the-art subjective visual effect, and the signal-noise-ratio (SNR is also objectively improved by 0.3–0.6 dB.

  11. Improved deadzone modeling for bivariate wavelet shrinkage-based image denoising

    Science.gov (United States)

    DelMarco, Stephen

    2016-05-01

    Modern image processing performed on-board low Size, Weight, and Power (SWaP) platforms, must provide high- performance while simultaneously reducing memory footprint, power consumption, and computational complexity. Image preprocessing, along with downstream image exploitation algorithms such as object detection and recognition, and georegistration, place a heavy burden on power and processing resources. Image preprocessing often includes image denoising to improve data quality for downstream exploitation algorithms. High-performance image denoising is typically performed in the wavelet domain, where noise generally spreads and the wavelet transform compactly captures high information-bearing image characteristics. In this paper, we improve modeling fidelity of a previously-developed, computationally-efficient wavelet-based denoising algorithm. The modeling improvements enhance denoising performance without significantly increasing computational cost, thus making the approach suitable for low-SWAP platforms. Specifically, this paper presents modeling improvements to the Sendur-Selesnick model (SSM) which implements a bivariate wavelet shrinkage denoising algorithm that exploits interscale dependency between wavelet coefficients. We formulate optimization problems for parameters controlling deadzone size which leads to improved denoising performance. Two formulations are provided; one with a simple, closed form solution which we use for numerical result generation, and the second as an integral equation formulation involving elliptic integrals. We generate image denoising performance results over different image sets drawn from public domain imagery, and investigate the effect of wavelet filter tap length on denoising performance. We demonstrate denoising performance improvement when using the enhanced modeling over performance obtained with the baseline SSM model.

  12. Adaptively wavelet-based image denoising algorithm with edge preserving

    Institute of Scientific and Technical Information of China (English)

    Yihua Tan; Jinwen Tian; Jian Liu

    2006-01-01

    @@ A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband.Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.

  13. Nonlinear Filter Based Image Denoising Using AMF Approach

    CERN Document Server

    Thivakaran, T K

    2010-01-01

    This paper proposes a new technique based on nonlinear Adaptive Median filter (AMF) for image restoration. Image denoising is a common procedure in digital image processing aiming at the removal of noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. This procedure is traditionally performed in the spatial or frequency domain by filtering. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly e...

  14. De-noising of digital image correlation based on stationary wavelet transform

    Science.gov (United States)

    Guo, Xiang; Li, Yulong; Suo, Tao; Liang, Jin

    2017-03-01

    In this paper, a stationary wavelet transform (SWT) based method is proposed to de-noise the digital image with the light noise, and the SWT de-noise algorithm is presented after the analyzing of the light noise. By using the de-noise algorithm, the method was demonstrated to be capable of providing accurate DIC measurements in the light noise environment. The verification, comparative and realistic experiments were conducted using this method. The result indicate that the de-noise method can be applied to the full-field strain measurement under the light interference with a high accuracy and stability.

  15. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising

    OpenAIRE

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient s...

  16. Boosting of Image Denoising Algorithms

    OpenAIRE

    Romano, Yaniv; Elad, Michael

    2015-01-01

    In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following "SOS" procedure: (i) (S)trengthen the signal by adding the previous denoised image to the degraded input image, (ii) (O)perate the denoising method on the strengthened image, and (iii) (S)ubtract the previous denoised image from the restored signal-strengthened outcome. The convergence of this process is studied for the K-SVD image ...

  17. An adaptive image denoising method based on local parameters optimization

    Indian Academy of Sciences (India)

    Hari Om; Mantosh Biswas

    2014-08-01

    In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods:Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.

  18. Improving performance of wavelet-based image denoising algorithm using complex diffusion process

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara; Korhonen, Jari

    2012-01-01

    Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges......). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image...... using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the denoised images...

  19. Dual tree complex wavelet transform based denoising of optical microscopy images.

    Science.gov (United States)

    Bal, Ufuk

    2012-12-01

    Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.

  20. Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images.

    Science.gov (United States)

    Chen, Qiang; de Sisternes, Luis; Leng, Theodore; Rubin, Daniel L

    2015-06-01

    Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging--a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patch-based methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patch-based methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others.

  1. [A new wavelet image de-noising method based on new threshold function].

    Science.gov (United States)

    Xing, Guoquan; Ye, Huashan; Zhang, Yuxia; Yan, Yu

    2013-08-01

    In order to improve image de-noising effect,a new threshold function de-noising method based on wavelet analysis was proposed, which can overcome the continuity problem of the hard-threshold function, and eliminate the constant deviation of the soft one by constructing a new threshold function. Experimental results showed that the new threshold function could obtain higher peak signal to noise ratio (PSNR) in image de-nosing. A better denoising effect could be obtained compared with the hard-threshold function, the soft one, the semi-soft one, the cubic polynomial interpolation semi-soft one, and the asymptotic semi-soft one.

  2. A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

    Science.gov (United States)

    Rubel, Aleksey S.; Lukin, Vladimir V.; Egiazarian, Karen O.

    2015-03-01

    Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

  3. A hybrid method for image Denoising based on Wavelet Thresholding and RBF network

    Directory of Open Access Journals (Sweden)

    Sandeep Dubey

    2012-06-01

    Full Text Available Digital image denoising is crucial part of image pre-processing. The application of denoising process in satellite image data and also in television broadcasting. Image data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compression. Thus, denoising is often a necessary and the first step to be taken before the images data is analyzed. In this paper we proposed a novel methodology for image denoising. Image denoising method based on wavelet transform and radial basis neural network and also used concept of soft thresholding. Wavelet transform decomposed image in to different layers, the decomposed layer differentiate by horizontal, vertical and diagonal. For the test of our hybrid method, we used noise image dataset. This data provided by UCI machine learning website. Our proposed method compare with traditional method and our base paper method and getting better comparative result.

  4. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising.

    Science.gov (United States)

    Khanian, Maryam; Feizi, Awat; Davari, Ali

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

  5. An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising

    Science.gov (United States)

    Khanian, Maryam; Feizi, Awat; Davari, Ali

    2014-01-01

    Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim. PMID:24696809

  6. Image Denoising Using Sure-Based Adaptive Thresholding in Directionlet Domain

    Directory of Open Access Journals (Sweden)

    Sethunadh R

    2012-12-01

    Full Text Available The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality.

  7. Image denoising using local tangent space alignment

    Science.gov (United States)

    Feng, JianZhou; Song, Li; Huo, Xiaoming; Yang, XiaoKang; Zhang, Wenjun

    2010-07-01

    We propose a novel image denoising approach, which is based on exploring an underlying (nonlinear) lowdimensional manifold. Using local tangent space alignment (LTSA), we 'learn' such a manifold, which approximates the image content effectively. The denoising is performed by minimizing a newly defined objective function, which is a sum of two terms: (a) the difference between the noisy image and the denoised image, (b) the distance from the image patch to the manifold. We extend the LTSA method from manifold learning to denoising. We introduce the local dimension concept that leads to adaptivity to different kind of image patches, e.g. flat patches having lower dimension. We also plug in a basic denoising stage to estimate the local coordinate more accurately. It is found that the proposed method is competitive: its performance surpasses the K-SVD denoising method.

  8. Low-dose computed tomography image denoising based on joint wavelet and sparse representation.

    Science.gov (United States)

    Ghadrdan, Samira; Alirezaie, Javad; Dillenseger, Jean-Louis; Babyn, Paul

    2014-01-01

    Image denoising and signal enhancement are the most challenging issues in low dose computed tomography (CT) imaging. Sparse representational methods have shown initial promise for these applications. In this work we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. Our results along with the computational efficiency of the proposed algorithm clearly demonstrates the improvement of the proposed algorithm over other clustering based sparse representation (CSR) and K-SVD methods.

  9. A new adaptive algorithm for image denoising based on curvelet transform

    Science.gov (United States)

    Chen, Musheng; Cai, Zhishan

    2013-10-01

    The purpose of this paper is to study a method of denoising images corrupted with additive white Gaussian noise. In this paper, the application of the time invariant discrete curvelet transform for noise reduction is considered. In curvelet transform, the frame elements are indexed by scale, orientation and location parameters. It is designed to represent edges and the singularities along curved paths more efficiently than the wavelet transform. Therefore, curvelet transform can get better results than wavelet method in image denoising. In general, image denoising imposes a compromise between noise reduction and preserving significant image details. To achieve a good performance in this respect, an efficient and adaptive image denoising method based on curvelet transform is presented in this paper. Firstly, the noisy image is decomposed into many levels to obtain different frequency sub-bands by curvelet transform. Secondly, efficient and adaptive threshold estimation based on generalized Gaussian distribution modeling of sub-band coefficients is used to remove the noisy coefficients. The choice of the threshold estimation is carried out by analyzing the standard deviation and threshold. Ultimately, invert the multi-scale decomposition to reconstruct the denoised image. Here, to prove the performance of the proposed method, the results are compared with other existent algorithms such as hard and soft threshold based on wavelet. The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that curvelet transform can achieve a better performance than the wavelet transform in image denoising.

  10. Quantum Boolean image denoising

    Science.gov (United States)

    Mastriani, Mario

    2015-05-01

    A quantum Boolean image processing methodology is presented in this work, with special emphasis in image denoising. A new approach for internal image representation is outlined together with two new interfaces: classical to quantum and quantum to classical. The new quantum Boolean image denoising called quantum Boolean mean filter works with computational basis states (CBS), exclusively. To achieve this, we first decompose the image into its three color components, i.e., red, green and blue. Then, we get the bitplanes for each color, e.g., 8 bits per pixel, i.e., 8 bitplanes per color. From now on, we will work with the bitplane corresponding to the most significant bit (MSB) of each color, exclusive manner. After a classical-to-quantum interface (which includes a classical inverter), we have a quantum Boolean version of the image within the quantum machine. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside image processing too. After filtering of the inverted version of MSB (inside quantum machine), the result passes through a quantum-classical interface (which involves another classical inverter) and then proceeds to reassemble each color component and finally the ending filtered image. Finally, we discuss the more appropriate metrics for image denoising in a set of experimental results.

  11. Denoising Of Hyperspectral Image

    Directory of Open Access Journals (Sweden)

    Ashumati Dhuppe

    2014-05-01

    Full Text Available The amount of noise included in a Hyperspectral images limits its application and has a negative impact on Hyperspectral image classification, unmixing, target detection, so on. Hyperspectral imaging (HSI systems can acquire both spectral and spatial information of ground surface simultaneously and have been used in a variety of applications such as object detection, material identification, land cover classification etc. In Hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high noise intensity bands & preserve the detailed information in the low noise intensity bands, the denoising strength should be adaptively adjusted with noise intensity in different bands. We propose a Hyperspectral image denoising algorithms employing a spectral spatial adaptive total variation (TV model, in which the spectral noise difference & spatial information differences are both considered in the process of noise reduction.

  12. [A novel denoising approach to SVD filtering based on DCT and PCA in CT image].

    Science.gov (United States)

    Feng, Fuqiang; Wang, Jun

    2013-10-01

    Because of various effects of the imaging mechanism, noises are inevitably introduced in medical CT imaging process. Noises in the images will greatly degrade the quality of images and bring difficulties to clinical diagnosis. This paper presents a new method to improve singular value decomposition (SVD) filtering performance in CT image. Filter based on SVD can effectively analyze characteristics of the image in horizontal (and/or vertical) directions. According to the features of CT image, we can make use of discrete cosine transform (DCT) to extract the region of interest and to shield uninterested region so as to realize the extraction of structure characteristics of the image. Then we transformed SVD to the image after DCT, constructing weighting function for image reconstruction adaptively weighted. The algorithm for the novel denoising approach in this paper was applied in CT image denoising, and the experimental results showed that the new method could effectively improve the performance of SVD filtering.

  13. Dictionary-Based Image Denoising by Fused-Lasso Atom Selection

    Directory of Open Access Journals (Sweden)

    Ao Li

    2014-01-01

    Full Text Available We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.

  14. Patch-based denoising method using low-rank technique and targeted database for optical coherence tomography image.

    Science.gov (United States)

    Liu, Xiaoming; Yang, Zhou; Wang, Jia; Liu, Jun; Zhang, Kai; Hu, Wei

    2017-01-01

    Image denoising is a crucial step before performing segmentation or feature extraction on an image, which affects the final result in image processing. In recent years, utilizing the self-similarity characteristics of the images, many patch-based image denoising methods have been proposed, but most of them, named the internal denoising methods, utilized the noisy image only where the performances are constrained by the limited information they used. We proposed a patch-based method, which uses a low-rank technique and targeted database, to denoise the optical coherence tomography (OCT) image. When selecting the similar patches for the noisy patch, our method combined internal and external denoising, utilizing the other images relevant to the noisy image, in which our targeted database is made up of these two kinds of images and is an improvement compared with the previous methods. Next, we leverage the low-rank technique to denoise the group matrix consisting of the noisy patch and the corresponding similar patches, for the fact that a clean image can be seen as a low-rank matrix and rank of the noisy image is much larger than the clean image. After the first-step denoising is accomplished, we take advantage of Gabor transform, which considered the layer characteristic of the OCT retinal images, to construct a noisy image before the second step. Experimental results demonstrate that our method compares favorably with the existing state-of-the-art methods.

  15. Patch Similarity Modulus and Difference Curvature Based Fourth-Order Partial Differential Equation for Image Denoising

    Directory of Open Access Journals (Sweden)

    Yunjiao Bai

    2015-01-01

    Full Text Available The traditional fourth-order nonlinear diffusion denoising model suffers the isolated speckles and the loss of fine details in the processed image. For this reason, a new fourth-order partial differential equation based on the patch similarity modulus and the difference curvature is proposed for image denoising. First, based on the intensity similarity of neighbor pixels, this paper presents a new edge indicator called patch similarity modulus, which is strongly robust to noise. Furthermore, the difference curvature which can effectively distinguish between edges and noise is incorporated into the denoising algorithm to determine the diffusion process by adaptively adjusting the size of the diffusion coefficient. The experimental results show that the proposed algorithm can not only preserve edges and texture details, but also avoid isolated speckles and staircase effect while filtering out noise. And the proposed algorithm has a better performance for the images with abundant details. Additionally, the subjective visual quality and objective evaluation index of the denoised image obtained by the proposed algorithm are higher than the ones from the related methods.

  16. Improved DCT-based nonlocal means filter for MR images denoising.

    Science.gov (United States)

    Hu, Jinrong; Pu, Yifei; Wu, Xi; Zhang, Yi; Zhou, Jiliu

    2012-01-01

    The nonlocal means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter based on the discrete cosine transform (DCT). Instead of computing similarity weights using the gray level information directly, the proposed method calculates similarity weights in the DCT subspace of neighborhood. Due to promising characteristics of DCT, such as low data correlation and high energy compaction, the proposed filter is naturally endowed with more accurate estimation of weights thus enhances denoising effectively. The performance of the proposed filter is evaluated qualitatively and quantitatively together with two other NLM filters, namely, the original NLM filter and the unbiased NLM (UNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance in MRI compared to the others.

  17. Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

    Directory of Open Access Journals (Sweden)

    Liang Xu

    2014-01-01

    Full Text Available We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN, and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects.

  18. A New Adaptive Diffusive Function for Magnetic Resonance Imaging Denoising Based on Pixel Similarity.

    Science.gov (United States)

    Heydari, Mostafa; Karami, Mohammad Reza

    2015-01-01

    Although there are many methods for image denoising, but partial differential equation (PDE) based denoising attracted much attention in the field of medical image processing such as magnetic resonance imaging (MRI). The main advantage of PDE-based denoising approach is laid in its ability to smooth image in a nonlinear way, which effectively removes the noise, as well as preserving edge through anisotropic diffusion controlled by the diffusive function. This function was first introduced by Perona and Malik (P-M) in their model. They proposed two functions that are most frequently used in PDE-based methods. Since these functions consider only the gradient information of a diffused pixel, they cannot remove noise in noisy images with low signal-to-noise (SNR). In this paper we propose a modified diffusive function with fractional power that is based on pixel similarity to improve P-M model for low SNR. We also will show that our proposed function will stabilize the P-M method. As experimental results show, our proposed function that is modified version of P-M function effectively improves the SNR and preserves edges more than P-M functions in low SNR.

  19. Biomedical images texture detail denoising based on PDE

    Science.gov (United States)

    Chen, Guan-nan; Pan, Jian-ji; Li, Chao; Chen, Rong; Lin, Ju-qiang; Yan, Kun-tao; Huang, Zu-fang

    2009-08-01

    Biomedical images denosing based on Partial Differential Equation are well-known for their good processing results. General denosing methods based on PDE can remove the noises of images with gentle changes and preserve more structure details of edges, but have a poor effectiveness on the denosing of biomedical images with many texture details. This paper attempts to make an overview of biomedical images texture detail denosing based on PDE. Subsequently, Three kinds of important image denosing schemes are introduced in this paper: one is image denosing based on the adaptive parameter estimation total variation model, which denosing the images based on region energy distribution; the second is using G norm on the perception scale, which provides a more intuitive understanding of this norm; the final is multi-scale denosing decomposition. The above methods involved can preserve more structures of biomedical images texture detail. Furthermore, this paper demonstrates the applications of those three methods. In the end, the future trend of biomedical images texture detail denosing Based on PDE is pointed out.

  20. Image denoising using new pixon representation based on fuzzy filtering and partial differential equations

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Nikpour, Mohsen

    2012-01-01

    In this paper, we have proposed two extensions to pixon-based image modeling. The first one is using bicubic interpolation instead of bilinear interpolation and the second one is using fuzzy filtering method, aiming to improve the quality of the pixonal image. Finally, partial differential...... equations (PDEs) are applied on the pixonal image for noise removing. The proposed algorithm has been examined on variety of standard images and their performance compared with the existing algorithms. Experimental results show that in comparison with the other existing methods, the proposed algorithm has...... a better performance in denoising and preserving image edges....

  1. Denoising of brain MRI images using modified PDE based on pixel similarity

    Science.gov (United States)

    Jin, Renchao; Song, Enmin; Zhang, Lijuan; Min, Zhifang; Xu, Xiangyang; Huang, Chih-Cheng

    2008-03-01

    Although various image denoising methods such as PDE-based algorithms have made remarkable progress in the past years, the trade-off between noise reduction and edge preservation is still an interesting and difficult problem in the field of image processing and analysis. A new image denoising algorithm, using a modified PDE model based on pixel similarity, is proposed to deal with the problem. The pixel similarity measures the similarity between two pixels. Then the neighboring consistency of the center pixel can be calculated. Informally, if a pixel is not consistent enough with its surrounding pixels, it can be considered as a noise, but an extremely strong inconsistency suggests an edge. The pixel similarity is a probability measure, its value is between 0 and 1. According to the neighboring consistency of the pixel, a diffusion control factor can be determined by a simple thresholding rule. The factor is combined into the primary partial differential equation as an adjusting factor for controlling the speed of diffusion for different type of pixels. An evaluation of the proposed algorithm on the simulated brain MRI images was carried out. The initial experimental results showed that the new algorithm can smooth the MRI images better while keeping the edges better and achieve higher peak signal to noise ratio (PSNR) comparing with several existing denoising algorithms.

  2. Image Denoising based on Fourth-Order Partial Differential Equations: A Survey

    Directory of Open Access Journals (Sweden)

    Anand Swaroop Khare

    2013-03-01

    Full Text Available Reduction of noise is essential especially in the field of image processing. Several researchers are continuously working in this direction and provide some good insights, but still there are lot of scope in this field. Noise mixed with image is harmful for image processing. In this paper we survey several aspects of image denoising and fourth-order partial differential equation. We also discuss several traditional methodology used with their advantages and disadvantages. We also provide a deep analysis based on the literature work from the previous research.

  3. Normal Inverse Gaussian Model-Based Image Denoising in the NSCT Domain

    Directory of Open Access Journals (Sweden)

    Jian Jia

    2015-01-01

    Full Text Available The objective of image denoising is to retain useful details while removing as much noise as possible to recover an original image from its noisy version. This paper proposes a novel normal inverse Gaussian (NIG model-based method that uses a Bayesian estimator to carry out image denoising in the nonsubsampled contourlet transform (NSCT domain. In the proposed method, the NIG model is first used to describe the distributions of the image transform coefficients of each subband in the NSCT domain. Then, the corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory. Finally, optimal linear interpolation thresholding algorithm (OLI-Shrink is employed to guarantee a gentler thresholding effect. The results of comparative experiments conducted indicate that the denoising performance of our proposed method in terms of peak signal-to-noise ratio is superior to that of several state-of-the-art methods, including BLS-GSM, K-SVD, BivShrink, and BM3D. Further, the proposed method achieves structural similarity (SSIM index values that are comparable to those of the block-matching 3D transformation (BM3D method.

  4. Digital Image Watermarking Based On Gradient Direction Quantization and Denoising Using Guided Image Filtering

    Directory of Open Access Journals (Sweden)

    I.Kullayamma

    2016-05-01

    Full Text Available Digital watermarking is the art of hiding of information or data in documents, where the embedded information or data can be extracted to resist copyright violation or to verify the uniqueness of a document which leads to security. Protecting the digital content has become a major issue for content owners and service providers. Watermarking using gradient direction quantization is based on the uniform quantization of the direction of gradient vectors, which is called gradient direction watermarking (GDWM. In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. The proposed scheme has the advantages of increased invisibility and robustness to amplitude scaling effects. The DWT coefficients are modified to quantize the gradient direction based on the on the derived relationship between the changes in the coefficients and the change in the gradient direction. In this paper, we propose a novel explicit image filter called guided filter. It is derived from a local linear model that computes the filtering output using the content of guidance image, which can be the input image itself or any other different image. The guided filter naturally has a fast and non approximate linear time algorithm, regardless of the kernel size and the intensity range. Finally, we show simulation results of denoising method using guided image filtering over bilateral filtering

  5. Normalized iterative denoising ghost imaging based on the adaptive threshold

    Science.gov (United States)

    Li, Gaoliang; Yang, Zhaohua; Zhao, Yan; Yan, Ruitao; Liu, Xia; Liu, Baolei

    2017-02-01

    An approach for improving ghost imaging (GI) quality is proposed. In this paper, an iteration model based on normalized GI is built through theoretical analysis. An adaptive threshold value is selected in the iteration model. The initial value of the iteration model is estimated as a step to remove the correlated noise. The simulation and experimental results reveal that the proposed strategy reconstructs a better image than traditional and normalized GI, without adding complexity. The NIDGI-AT scheme does not require prior information regarding the object, and can also choose the threshold adaptively. More importantly, the signal-to-noise ratio (SNR) of the reconstructed image is greatly improved. Therefore, this methodology represents another step towards practical real-world applications.

  6. Region-based adaptive anisotropic diffusion for image enhancement and denoising

    Science.gov (United States)

    Wang, Yi; Niu, Ruiqing; Zhang, Liangpei; Shen, Huanfeng

    2010-11-01

    A novel region-based adaptive anisotropic diffusion (RAAD) is presented for image enhancement and denoising. The main idea of this algorithm is to perform the region-based adaptive segmentation. To this end, we use the eigenvalue difference of the structure tensor of each pixel to classify an image into homogeneous detail, and edge regions. According to the different types of regions, a variable weight is incorporated into the anisotropic diffusion partial differential equation for compromising the forward and backward diffusion, so that our algorithm can adaptively encourage strong smoothing in homogeneous regions and suitable sharpening in detail and edge regions. Furthermore, we present an adaptive gradient threshold selection strategy. We suggest that the optimal gradient threshold should be estimated as the mean of local intensity differences on the homogeneous regions. In addition, we modify the anisotropic diffusion discrete scheme by taking into account edge orientations. We believe our algorithm to be a novel mechanism for image enhancement and denoising. Qualitative experiments, based on various general digital images and several T1- and T2-weighted magnetic resonance simulated images, show significant improvements when the RAAD algorithm is used versus the existing anisotropic diffusion and the previous forward and backward diffusion algorithms for enhancing edge features and improving image contrast. Quantitative analyses, based on peak signal-to-noise ratio, the universal image quality index, and the structural similarity confirm the superiority of the proposed algorithm.

  7. Image Segmentation and Denoising Based on Shrira-Pesenson Equation

    Science.gov (United States)

    Pesenson, M.; Moshir, M.; Makovoz, D.; Frayer, D.; Henderson, D.

    2005-12-01

    We propose a nonlinear partial differential equation to control the trade-off between smoothing and segmentation of images. Its solutions approximate discontinuities, thus leading to detection of sharp boundaries in images. The performance of the approach is evaluated by applying it to images obtained by the Multiband Imaging Photometer for Spitzer (MIPS), 70 micron imaging band.

  8. Fractional Partial Differential Equation: Fractional Total Variation and Fractional Steepest Descent Approach-Based Multiscale Denoising Model for Texture Image

    Directory of Open Access Journals (Sweden)

    Yi-Fei Pu

    2013-01-01

    Full Text Available The traditional integer-order partial differential equation-based image denoising approaches often blur the edge and complex texture detail; thus, their denoising effects for texture image are not very good. To solve the problem, a fractional partial differential equation-based denoising model for texture image is proposed, which applies a novel mathematical method—fractional calculus to image processing from the view of system evolution. We know from previous studies that fractional-order calculus has some unique properties comparing to integer-order differential calculus that it can nonlinearly enhance complex texture detail during the digital image processing. The goal of the proposed model is to overcome the problems mentioned above by using the properties of fractional differential calculus. It extended traditional integer-order equation to a fractional order and proposed the fractional Green’s formula and the fractional Euler-Lagrange formula for two-dimensional image processing, and then a fractional partial differential equation based denoising model was proposed. The experimental results prove that the abilities of the proposed denoising model to preserve the high-frequency edge and complex texture information are obviously superior to those of traditional integral based algorithms, especially for texture detail rich images.

  9. GPU-Based Block-Wise Nonlocal Means Denoising for 3D Ultrasound Images

    Directory of Open Access Journals (Sweden)

    Liu Li

    2013-01-01

    Full Text Available Speckle suppression plays an important role in improving ultrasound (US image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU- based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.

  10. Denoising functional MR images : A comparison of wavelet denoising and Gaussian smoothing

    NARCIS (Netherlands)

    Wink, Alle Meije; Roerdink, Jos B.T.M.

    2004-01-01

    We present a general wavelet-based denoising scheme for functional magnetic resonance imaging (fMRI) data and compare it to Gaussian smoothing, the traditional denoising method used in fMRI analysis. One-dimensional WaveLab thresholding routines were adapted to two-dimensional images, and applied to

  11. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging

    Energy Technology Data Exchange (ETDEWEB)

    Boussion, N.; Cheze Le Rest, C.; Hatt, M.; Visvikis, D. [INSERM, U650, Laboratoire de Traitement de l' Information Medicale (LaTIM) CHU MORVAN, Brest (France)

    2009-07-15

    Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising. Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods. Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images. The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography. (orig.)

  12. IMAGE WAVELET DENOISING USING THE ROBUST LOCAL THRESHOLD

    Institute of Scientific and Technical Information of China (English)

    LinKezheng; ZhouHongyu; 等

    2002-01-01

    This paper suggests a scheme of image denoising based on two-dimensional discrete wavelet transform.The denoising algorithm is described with some operatiors.By thresholding the wavelet transform coefficients of noisy images, the original image can be reconstructed cor-rectly.Different threshold selections and thresholding methods are discussed.A new robust local threshold scheme is proposed.Quantifying the performance of image denoising schemes by using the mean square error, the performance of the robust local threshold scheme is demonstrated and is compared with the universal threshold scheme.The experiment shows that image denoising using the robust local threshold performs better than that using the universal threshold.

  13. IMAGE WAVELET DENOISING USING THE ROBUST LOCAL THRESHOLD

    Institute of Scientific and Technical Information of China (English)

    Lin Kezheng; Zhou Hongyu; Li Dianpu

    2002-01-01

    This paper suggests a scheme of image denoising based on two-dimensional discrete wavelet transform. The denoising algorithm is described with some operators. By thresholding the wavelet transform coefficients of noisy images, the original image can be reconstructed correctly. Different threshold selections and thresholding methods are discussed. A new robust local threshold scheme is proposed. Quantifying the performance of image denoising schemes by using the mean square error, the performance of the robust local threshold scheme is demonstrated and is compared with the universal threshold scheme. The experiment shows that image denoising using the robust local threshold performs better than that using the universal threshold.

  14. Two image denoising approaches based on wavelet neural network and particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Yunyi Yan; Baolong Guo

    2007-01-01

    Two image denoising approaches based on wavelet neural network (WNN) optimized by particle swarm optimization (PSO) are proposed. The noisy image is filtered by the modified median filtering (MMF).Feature values are extracted based on the MMF and then normalized in order to avoid data scattering. In approach 1, WNN is used to tell those uncorrupted but filtered by MMF and then the pixels are restored to their original values while other pixels will retain. In approach 2, WNN distinguishes the corrupted pixels and then these pixels are replaced by MMF results while other pixels retain. WNN can be seen as a classifier to distinguish the corrupted or uncorrupted pixels from others in both approaches. PSO is adopted to optimize and train the WNN for its low requirements and easy employment. Experiments have shown that in terms of peak signal-to-noise ratio (PSNR) and subjective image quality, both proposed approaches are superior to traditional median filtering.

  15. Image denoising using the squared eigenfunctions of the Schrodinger operator

    KAUST Repository

    Kaisserli, Zineb

    2015-02-02

    This study introduces a new image denoising method based on the spectral analysis of the semi-classical Schrodinger operator. The noisy image is considered as a potential of the Schrodinger operator, and the denoised image is reconstructed using the discrete spectrum of this operator. First results illustrating the performance of the proposed approach are presented and compared to the singular value decomposition method.

  16. Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm.

    Science.gov (United States)

    Liu, Qianshun; Bai, Jian; Yu, Feihong

    2014-11-10

    In an effort to improve compressive sensing and spare signal reconstruction by way of the backtracking-based adaptive orthogonal matching pursuit (BAOMP), a new sparse coding algorithm called improved adaptive backtracking-based OMP (ABOMP) is proposed in this study. Many aspects have been improved compared to the original BAOMP method, including replacing the fixed threshold with an adaptive one, adding residual feedback and support set verification, and others. Because of these ameliorations, the proposed algorithm can more precisely choose the atoms. By adding the adaptive step-size mechanism, it requires much less iteration and thus executes more efficiently. Additionally, a simple but effective contrast enhancement method is also adopted to further improve the denoising results and visual effect. By combining the IABOMP algorithm with the state-of-art dictionary learning algorithm K-SVD, the proposed algorithm achieves better denoising effects for astronomical images. Numerous experimental results show that the proposed algorithm performs successfully and effectively on Gaussian and Poisson noise removal.

  17. Energy-based adaptive orthogonal FRIT and its application in image denoising

    Institute of Scientific and Technical Information of China (English)

    LIU YunXia; PENG YuHua; QU HuaiJing; YiN Yong

    2007-01-01

    Efficient representation of linear singularities is discussed in this paper. We analyzed the relationship between the "wrap around" effect and the distribution of FRAT (Finite Radon Transform) coefficients first, and then based on study of some properties of the columnwisely FRAT reconstruction procedure, we proposed an energy-based adaptive orthogonal FRIT scheme (EFRIT). Experiments using nonlinear approximation show its superiority in energy concentration over both Discrete Wavelet Transform (DWT) and Finite Ridgelet Transform (FRIT). Furthermore, we have modeled the denoising problem and proposed a novel threshold selecting method. Experiments carried out on images containing strong linear singularities and texture components with varying levels of addictive white Gaussian noise show that our method achieves prominent improvement in terms of both SNR and visual quality as compared with that of DWT and FRIT.

  18. Non parametric denoising methods based on wavelets: Application to electron microscopy images in low exposure time

    Energy Technology Data Exchange (ETDEWEB)

    Soumia, Sid Ahmed, E-mail: samasoumia@hotmail.fr [Science and Technology Faculty, El Bachir El Ibrahimi University, BordjBouArreridj (Algeria); Messali, Zoubeida, E-mail: messalizoubeida@yahoo.fr [Laboratory of Electrical Engineering(LGE), University of M' sila (Algeria); Ouahabi, Abdeldjalil, E-mail: abdeldjalil.ouahabi@univ-tours.fr [Polytechnic School, University of Tours (EPU - PolytechTours), EPU - Energy and Electronics Department (France); Trepout, Sylvain, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr; Messaoudi, Cedric, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr; Marco, Sergio, E-mail: sylvain.trepout@curie.fr, E-mail: cedric.messaoudi@curie.fr, E-mail: sergio.marco@curie.fr [INSERMU759, University Campus Orsay, 91405 Orsay Cedex (France)

    2015-01-13

    The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the

  19. Remote sensing image denoising by using discrete multiwavelet transform techniques

    Science.gov (United States)

    Wang, Haihui; Wang, Jun; Zhang, Jian

    2006-01-01

    We present a new method by using GHM discrete multiwavelet transform in image denoising on this paper. The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data. The method of signal denoising via wavelet thresholding was popularized. Multiwavelets have recently been introduced and they offer simultaneous orthogonality, symmetry and short support. This property makes multiwavelets more suitable for various image processing applications, especially denoising. It is based on thresholding of multiwavelet coefficients arising from the standard scalar orthogonal wavelet transform. It takes into account the covariance structure of the transform. Denoising of images via thresholding of the multiwavelet coefficients result from preprocessing and the discrete multiwavelet transform can be carried out by treating the output in this paper. The form of the threshold is carefully formulated and is the key to the excellent results obtained in the extensive numerical simulations of image denoising. We apply the multiwavelet-based to remote sensing image denoising. Multiwavelet transform technique is rather a new method, and it has a big advantage over the other techniques that it less distorts spectral characteristics of the image denoising. The experimental results show that multiwavelet based image denoising schemes outperform wavelet based method both subjectively and objectively.

  20. An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

    Directory of Open Access Journals (Sweden)

    Min Wang

    2015-01-01

    Full Text Available This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD, with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts, with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

  1. Comparison of PDE-based non-linear anistropic diffusion techniques for image denoising

    Science.gov (United States)

    Weeratunga, Sisira K.; Kamath, Chandrika

    2003-05-01

    PDE-based, non-linear diffusion techniques are an effective way to denoise images.In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.

  2. A Comparison of PDE-based Non-Linear Anisotropic Diffusion Techniques for Image Denoising

    Energy Technology Data Exchange (ETDEWEB)

    Weeratunga, S K; Kamath, C

    2003-01-06

    PDE-based, non-linear diffusion techniques are an effective way to denoise images. In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.

  3. Combining interior and exterior characteristics for remote sensing image denoising

    Science.gov (United States)

    Peng, Ni; Sun, Shujin; Wang, Runsheng; Zhong, Ping

    2016-04-01

    Remote sensing image denoising faces many challenges since a remote sensing image usually covers a wide area and thus contains complex contents. Using the patch-based statistical characteristics is a flexible method to improve the denoising performance. There are usually two kinds of statistical characteristics available: interior and exterior characteristics. Different statistical characteristics have their own strengths to restore specific image contents. Combining different statistical characteristics to use their strengths together may have the potential to improve denoising results. This work proposes a method combining statistical characteristics to adaptively select statistical characteristics for different image contents. The proposed approach is implemented through a new characteristics selection criterion learned over training data. Moreover, with the proposed combination method, this work develops a denoising algorithm for remote sensing images. Experimental results show that our method can make full use of the advantages of interior and exterior characteristics for different image contents and thus improve the denoising performance.

  4. Image Denoising based on Fourth-Order Partial Differential Equations: A Survey

    Directory of Open Access Journals (Sweden)

    Anand Swaroop Khare,

    2013-04-01

    Full Text Available Reduction of noise is essential especially in the fieldof image processing. Several researchers arecontinuously working in this direction and providesome good insights, but still there are lot of scope inthis field.Noise mixed with image is harmful forimage processing. Inthis paper we survey severalaspects of image denoising and fourth-order partialdifferential equation.We also discuss severaltraditional methodology used with their advantagesand disadvantages. We also provide a deep analysisbased on the literature work from the previousresearch.

  5. SPATIAL-VARIANT MORPHOLOGICAL FILTERS WITH NONLOCAL-PATCH-DISTANCE-BASED AMOEBA KERNEL FOR IMAGE DENOISING

    Directory of Open Access Journals (Sweden)

    Shuo Yang

    2015-01-01

    Full Text Available Filters of the Spatial-Variant amoeba morphology can preserve edges better, but with too much noise being left. For better denoising, this paper presents a new method to generate structuring elements for Spatially-Variant amoeba morphology.  The amoeba kernel in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. Compared to traditional amoeba kernel, the new one has more stable center and its shape can be less influenced by noise in pilot image. What’s more important is that the nonlocal processing approach can induce a couple of adjoint dilation and erosion, and combinations of them can construct adaptive opening, closing, alternating sequential filters, etc. By designing the new amoeba kernel, a family of morphological filters therefore is derived. Finally, this paper presents a series of results on both synthetic and real images along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing and noisy SAR image restoration.

  6. Result Analysis of Blur and Noise on Image Denoising based on PDE

    Directory of Open Access Journals (Sweden)

    Meenal Jain

    2012-12-01

    Full Text Available The effect of noise on image is still a challenging problem for researchers. Image Denoising has remained a fundamental problem in the field of image processing. Wavelets give a superior performance in image denoising due to properties such as sparsity and multi resolution structure. Many of the previous research use the basic noise reduction through image blurring. Blurring can be done locally, as in the Gaussian smoothing model or in anisotropic filtering; by calculus of variations; or in the frequency domain, such as Weiner filters. In this paper we proposed an image denoising method using partial differential equation. In our proposed approach we proposed three different approaches first is for blur, second is for noise and finally for blur and noise. These approaches are compared by Average absolute difference, signal to noise ratio (SNR, peak signal to noise ratio (PSNR, Image Fidelity and Mean square error. So we can achieve better result on different scenario. We also compare our result on the basis of the above five parameters and the result is better in comparison to the traditional technique.

  7. Astronomical Image Denoising Using Dictionary Learning

    CERN Document Server

    Beckouche, Simon; Fadili, Jalal

    2013-01-01

    Astronomical images suffer a constant presence of multiple defects that are consequences of the intrinsic properties of the acquisition equipments, and atmospheric conditions. One of the most frequent defects in astronomical imaging is the presence of additive noise which makes a denoising step mandatory before processing data. During the last decade, a particular modeling scheme, based on sparse representations, has drawn the attention of an ever growing community of researchers. Sparse representations offer a promising framework to many image and signal processing tasks, especially denoising and restoration applications. At first, the harmonics, wavelets, and similar bases and overcomplete representations have been considered as candidate domains to seek the sparsest representation. A new generation of algorithms, based on data-driven dictionaries, evolved rapidly and compete now with the off-the-shelf fixed dictionaries. While designing a dictionary beforehand leans on a guess of the most appropriate repre...

  8. Denoising approach for remote sensing image based on anisotropic diffusion and wavelet transform algorithm

    Science.gov (United States)

    Wang, Xiaojun; Lai, Weidong

    2011-08-01

    In this paper, a combined method have been put forward for one ASTER detected image with the wavelet filter to attenuate the noise and the anisotropic diffusion PDE(Partial Differential Equation) for further recovering image contrast. The model is verified in different noising background, since the remote sensing image usually contains salt and pepper, Gaussian as well as speckle noise. Considered the features that noise existing in wavelet domain, the wavelet filter with Bayesian estimation threshold is applied for recovering image contrast from the blurring background. The proposed PDE are performing an anisotropic diffusion in the orthogonal direction, thus preserving the edges during further denoising process. Simulation indicates that the combined algorithm can more effectively recover the blurred image from speckle and Gauss noise background than the only wavelet denoising method, while the denoising effect is also distinct when the pepper-salt noise has low intensity. The combined algorithm proposed in this article can be integrated in remote sensing image analyzing to obtain higher accuracy for environmental interpretation and pattern recognition.

  9. Image Denoising using Adaptive Thresholding in Framelet Transform Domain

    Directory of Open Access Journals (Sweden)

    R.Vidhya

    2012-09-01

    Full Text Available Noise will be unavoidable during image acquisition process and denosing is an essential step to improve the image quality. Image denoising involves the manipulation of the image data to produce a visually high quality image. Finding efficient image denoising methods is still valid challenge in image processing. Wavelet denoising attempts to remove the noise present in the imagery while preserving the image characteristics, regardless of its frequency content. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform in the decomposition stage which is suffering from shift variance. To overcome this, in this paper we proposed the denoising method which uses Framelet transform to decompose the image and performed shrinkage operation to eliminate the noise .The framework describes a comparative study of different thresholding techniques for image denoising in Framelet transform domain. The idea is to transform the data into the Framelet basis, example shrinkage followed by the inverse transform. In this work different shrinkage rules such as universal shrink(US,Visu shrink (VS, Minmax shrink(MS, Sure shrink(SS , Bayes shrink(BS and Normal shrink(NS were incorporated . Results based on different noise such as Gausssian noise, Poission noise , Salt and pepper noise and Speckle noise at (??=10,20 performed in this paper and peak signal to noise ratio (PSNR and Structural similarity index measure(SSIM as a measure of the quality of denoising was performed.

  10. Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials.

    Science.gov (United States)

    Ithapul, Vamsi K; Singh, Vikas; Okonkwo, Ozioma; Johnson, Sterling C

    2014-01-01

    There is growing body of research devoted to designing imaging-based biomarkers that identify Alzheimer's disease (AD) in its prodromal stage using statistical machine learning methods. Recently several authors investigated how clinical trials for AD can be made more efficient (i.e., smaller sample size) using predictive measures from such classification methods. In this paper, we explain why predictive measures given by such SVM type objectives may be less than ideal for use in the setting described above. We give a solution based on a novel deep learning model, randomized denoising autoencoders (rDA), which regresses on training labels y while also accounting for the variance, a property which is very useful for clinical trial design. Our results give strong improvements in sample size estimates over strategies based on multi-kernel learning. Also, rDA predictions appear to more accurately correlate to stages of disease. Separately, our formulation empirically shows how deep architectures can be applied in the large d, small n regime--the default situation in medical imaging. This result is of independent interest.

  11. Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials

    Science.gov (United States)

    Ithapu, Vamsi K.; Singh, Vikas; Okonkwo, Ozioma; Johnson, Sterling C.

    2015-01-01

    There is growing body of research devoted to designing imaging-based biomarkers that identify Alzheimer’s disease (AD) in its prodromal stage using statistical machine learning methods. Recently several authors investigated how clinical trials for AD can be made more efficient (i.e., smaller sample size) using predictive measures from such classification methods. In this paper, we explain why predictive measures given by such SVM type objectives may be less than ideal for use in the setting described above. We give a solution based on a novel deep learning model, randomized denoising autoencoders (rDA), which regresses on training labels y while also accounting for the variance, a property which is very useful for clinical trial design. Our results give strong improvements in sample size estimates over strategies based on multi-kernel learning. Also, rDA predictions appear to more accurately correlate to stages of disease. Separately, our formulation empirically shows how deep architectures can be applied in the large d, small n regime — the default situation in medical imaging. This result is of independent interest. PMID:25485413

  12. Image denoising via sparse and redundant representations over learned dictionaries.

    Science.gov (United States)

    Elad, Michael; Aharon, Michal

    2006-12-01

    We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.

  13. An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

    OpenAIRE

    Min Wang; Zhen Li; Xiangjun Duan; Wei Li

    2015-01-01

    This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with i...

  14. 基于小波分析的图像去噪%Image Denoising Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    李红; 解争龙

    2011-01-01

    针对图像去噪展开研究,结合均值滤波技术和小波分析技术,提出了使用高斯平滑滤波与小波局部阈值处理相结合的方法。首先对图像进行高斯平滑滤波,然后选取适当的小波阈值对小波系数进行处理、重构得到新的图像,并将去噪图像的峰值信噪比作为性能指标,仿真实验结果表明文中所用的方法去噪效果更佳,图像有着更好的视觉效果。%A method of image denoising technology based on the Gaussian filter and wavelet transformation is proposed. The wavelet threshold denoising is one of the major methods of denoising in the wavelet domain. Firstly, noised image was processed by Gaussian filter and was decomposed by wavelet transformation. Secondly, an appropriate threshold value and decomposed layer were selected. Finally, the image was reconstructed to the first layer and the reconstructed image was reconstructed to the second layer. Simulation results showed that the method not only could remove noise effectively, but also could get higher PSNR value and better visual effect compared with other methods.

  15. PDE-based nonlinear diffusion techniques for denoising scientific and industrial images: an empirical study

    Science.gov (United States)

    Weeratunga, Sisira K.; Kamath, Chandrika

    2002-05-01

    Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, we focus on one such approach, namely the use of non-linear diffusion operators. This approach has been studied extensively from a theoretical viewpoint ever since the 1987 work of Perona and Malik showed that non-linear filters outperformed the more traditional linear Canny edge detector. We complement this theoretical work by investigating the performance of several isotropic diffusion operators on test images from scientific domains. We explore the effects of various parameters such as the choice of diffusivity function, explicit and implicit methods for the discretization of the PDE, and approaches for the spatial discretization of the non-linear operator etc. We also compare these schemes with simple spatial filters and the more complex wavelet-based shrinkage techniques. Our empirical results show that, with an appropriate choice of parameters, diffusion-based schemes can be as effective as competitive techniques.

  16. Terahertz digital holography image denoising using stationary wavelet transform

    Science.gov (United States)

    Cui, Shan-Shan; Li, Qi; Chen, Guanghao

    2015-04-01

    Terahertz (THz) holography is a frontier technology in terahertz imaging field. However, reconstructed images of holograms are inherently affected by speckle noise, on account of the coherent nature of light scattering. Stationary wavelet transform (SWT) is an effective tool in speckle noise removal. In this paper, two algorithms for despeckling SAR images are implemented to THz images based on SWT, which are threshold estimation and smoothing operation respectively. Denoised images are then quantitatively assessed by speckle index. Experimental results show that the stationary wavelet transform has superior denoising performance and image detail preservation to discrete wavelet transform. In terms of the threshold estimation, high levels of decomposing are needed for better denoising result. The smoothing operation combined with stationary wavelet transform manifests the optimal denoising effect at single decomposition level, with 5×5 average filtering.

  17. Bilateral Filtering using Modified Fuzzy Clustering for Image Denoising

    Directory of Open Access Journals (Sweden)

    G.Vijaya,

    2011-01-01

    Full Text Available This paper presents a novel bilateral filtering using weighed fcm algorithm based on Gaussian kernel unction for image manipulations such as segmentation and denoising . Our proposed bilateral filteringconsists of the standard bilateral filter and the original Euclidean distance is replaced by a kernel – induced distance in the algorithm. We have applied the proposed filtering for image denoising with both the impulse and Gaussian random noise, which achieves better results than the bilateral filtering based denoising approaches, the Perona-Maliks anisotropic diffusion filter, the fuzzy vector median filter and the Non-Local Means filter.

  18. Literature Review of Image Denoising Methods

    Institute of Scientific and Technical Information of China (English)

    LIU Qian; YANG Xing-qiang; LI Yun-liang

    2014-01-01

    Image denoising is a fundamental and important task in image processing and computer vision fields. A lot of methods are proposed to reconstruct clean images from their noisy versions. These methods differ in both methodology and performance. On one hand, denoising methods can be classified into local and nonlocal methods. On the other hand, they can be marked as spatial and frequency domain methods. Sparse coding and low-rank are two popular techniques for denoising recently. This paper summarizes existing techniques and provides several promising directions for further studying in the future.

  19. Denoising CT Images using wavelet transform

    Directory of Open Access Journals (Sweden)

    Lubna Gabralla

    2015-05-01

    Full Text Available Image denoising is one of the most significant tasks especially in medical image processing, where the original images are of poor quality due the noises and artifacts introduces by the acquisition systems. In this paper, we propose a new image denoising scheme by modifying the wavelet coefficients using soft-thresholding method, we present a comparative study of different wavelet denoising techniques for CT images and we discuss the obtained results. The denoising process rejects noise by thresholding in the wavelet domain. The performance is evaluated using Peak Signal-to-Noise Ratio (PSNR and Mean Squared Error (MSE. Finally, Gaussian filter provides better PSNR and lower MSE values. Hence, we conclude that this filter is an efficient one for preprocessing medical images.

  20. Evaluation of non-local means based denoising filters for diffusion kurtosis imaging using a new phantom.

    Directory of Open Access Journals (Sweden)

    Min-Xiong Zhou

    Full Text Available Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI. This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM. Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE, Bias and standard deviation (Std. The result of the comparison based on the phantom was consistent with those based on real datasets.

  1. Image denoising by a direct variational minimization

    Directory of Open Access Journals (Sweden)

    Pilipović Stevan

    2011-01-01

    Full Text Available Abstract In this article we introduce a novel method for the image de-noising which combines a mathematically well-posdenes of the variational modeling with the efficiency of a patch-based approach in the field of image processing. It based on a direct minimization of an energy functional containing a minimal surface regularizer that uses fractional gradient. The minimization is obtained on every predefined patch of the image, independently. By doing so, we avoid the use of an artificial time PDE model with its inherent problems of finding optimal stopping time, as well as the optimal time step. Moreover, we control the level of image smoothing on each patch (and thus on the whole image by adapting the Lagrange multiplier using the information on the level of discontinuities on a particular patch, which we obtain by pre-processing. In order to reduce the average number of vectors in the approximation generator and still to obtain the minimal degradation, we combine a Ritz variational method for the actual minimization on a patch, and a complementary fractional variational principle. Thus, the proposed method becomes computationally feasible and applicable for practical purposes. We confirm our claims with experimental results, by comparing the proposed method with a couple of PDE-based methods, where we get significantly better denoising results specially on the oscillatory regions.

  2. Applications of discrete multiwavelet techniques to image denoising

    Science.gov (United States)

    Wang, Haihui; Peng, Jiaxiong; Wu, Wei; Ye, Bin

    2003-09-01

    In this paper, we present a new method by using 2-D discrete multiwavelet transform in image denoising. The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data. The method of signal denoising via wavelet thresholding was popularized. Multiwavelets have recently been introduced and they offer simultaneous orthogonality, symmetry and short support. This property makes multiwavelets more suitable for various image processing applications, especially denoising. It is based on thresholding of multiwavelet coefficients arising from the standard scalar orthogonal wavelet transform. It takes into account the covariance structure of the transform. Denoising is images via thresholding of the multiwavelet coefficients result from preprocessing and the discrete multiwavelet transform can be carried out by threating the output in this paper. The form of the threshold is carefully formulated and is the key to the excellent results obtained in the extensive numerical simulations of image denoising. The performances of multiwavelets are compared with those of scalar wavelets. Simulations reveal that multiwavelet based image denoising schemes outperform wavelet based method both subjectively and objectively.

  3. Study of Denoising Method of Images- A Review

    Directory of Open Access Journals (Sweden)

    Ravi Mohan Sairam

    2013-05-01

    Full Text Available This paper attempts to undertake the study of Denoising Methods. Different noise densities have been removed by using filters Wavelet based Methods. Fourier transform method is localized in frequency domain where the Wavelet transform method is localized in both frequency and spatial domain but both the above methods are not data adaptive .Independent Component Analysis (ICA is a higher order statistical tool for the analysis of multidimensional data with inherent data adaptiveness property. In This paper we try to presents a review of some significant work in the area of image denoising and finds the one is better for image denoising. Here, some popular approaches are classified into different groups .after that we conclude for best technique for Image Denoising

  4. Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jun-song; YU Jin-hui; MAO Guo-hong; YE Xiu-zi

    2006-01-01

    In this paper, a novel approach is proposed for denoising of Chinese calligraphy tablet documents. The method includes two phases: First, a partial differential equations (PDE) based the total variation model and Otsu thresholding method are used to preprocess the calligraphy document image. Second, a new method based on on-length statistics and structure characteristics of Chinese characters is proposed to remove some random and ant-like noises. This includes the optimal threshold selection from histogram of run-length probability density, and improved Hough transform algorithm for line shape noise detection and removal. Examples are given in the paper to demonstrate the proposed method.

  5. Image denoising using modified nonlinear diffusion approach

    Science.gov (United States)

    Upadhyay, Akhilesh R.; Talbar, Sanjay N.; Sontakke, Trimbak R.

    2006-01-01

    Partial Differential Equation (PDE) based, non-linear diffusion approaches are an effective way to denoise the images. In this paper, the work is extended to include anisotropic diffusion, where the diffusivity is a tensor valued function, which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. The diffusion tensor is a function of differential structure of the evolving image itself. Such a feedback leads to nonlinear diffusion filters. It shows improved performance in the presence of noise. The original anisotropic diffusion algorithm updates each point based on four nearest-neighbor differences, the progress of diffusion results in improved edges. In the proposed method the edges are better preserved because diffusion is controlled by the gray level differences of diagonal neighbors in addition to 4 nearest neighbors using coupled PDF formulation. The proposed algorithm gives excellent results for MRI images, Biomedical images and Fingerprint images with noise.

  6. An image denoising application using shearlets

    Science.gov (United States)

    Sevindir, Hulya Kodal; Yazici, Cuneyt

    2013-10-01

    Medical imaging is a multidisciplinary field related to computer science, electrical/electronic engineering, physics, mathematics and medicine. There has been dramatic increase in variety, availability and resolution of medical imaging devices for the last half century. For proper medical imaging highly trained technicians and clinicians are needed to pull out clinically pertinent information from medical data correctly. Artificial systems must be designed to analyze medical data sets either in a partially or even a fully automatic manner to fulfil the need. For this purpose there has been numerous ongoing research for finding optimal representations in image processing and computer vision [1, 18]. Medical images almost always contain artefacts and it is crucial to remove these artefacts to obtain healthy results. Out of many methods for denoising images, in this paper, two denoising methods, wavelets and shearlets, have been applied to mammography images. Comparing these two methods, shearlets give better results for denoising such data.

  7. 基于偏微分方程的舌图像去噪研究%Research of Tongue Image Denoising Based on Partial Differential Equation

    Institute of Scientific and Technical Information of China (English)

    唐苏湘; 汪仁煌; 韦玉科; 明俊峰; 何最红

    2012-01-01

    When denoising the tongue image, the details such as the edges and the veins are easy to be lost. Aiming at this problem, this paper proposes a new method to denoise the tongue image based on Partial Differential Equation(PDE). It applies median filtering, Gaussian filtering, P-M equation, regularization P-M equation and Coupling shock filtering combined complex diffusion filtering model to denoise the noising tongue images. Comparison result shows that the regularized P-M equation is the best method to denoise the tongue images. It has fast speed and good denoising result, and the best preserving for edges.%在对舌图像的去噪过程中,平滑噪声的同时容易丢失边缘和纹理等细节信息.为此,研究基于偏微分方程的舌图像去噪方法,分别采用中值滤波、高斯滤波、P-M方程、正则化P-M方程以及耦合冲击-复扩散滤波模型,对加噪舌图像进行滤波.比较结果表明,正则化P-M方程更适合舌图像的去噪处理,该方法处理速度快、去噪效果好,且能有效保护图像边缘.

  8. An adaptive nonlocal means scheme for medical image denoising

    Science.gov (United States)

    Thaipanich, Tanaphol; Kuo, C.-C. Jay

    2010-03-01

    Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this work, we investigate an adaptive denoising scheme based on the nonlocal (NL)-means algorithm for medical imaging applications. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means (ANL-means) denoising scheme has three unique features. First, it employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust classification of blocks in noisy images. Second, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity matching. Experimental results from both additive white Gaussian noise (AWGN) and Rician noise are given to demonstrate the superior performance of the proposed ANL denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison.

  9. Variational denoising of partly textured images by spatially varying constraints.

    Science.gov (United States)

    Gilboa, Guy; Sochen, Nir; Zeevi, Yehoshua Y

    2006-08-01

    Denoising algorithms based on gradient dependent regularizers, such as nonlinear diffusion processes and total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. We propose a mechanism that better preserves fine scale features in such denoising processes. A basic pyramidal structure-texture decomposition of images is presented and analyzed. A first level of this pyramid is used to isolate the noise and the relevant texture components in order to compute spatially varying constraints based on local variance measures. A variational formulation with a spatially varying fidelity term controls the extent of denoising over image regions. Our results show visual improvement as well as an increase in the signal-to-noise ratio over scalar fidelity term processes. This type of processing can be used for a variety of tasks in partial differential equation-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.

  10. 基于偏微分方程的图像去噪算法%Image denoising algorithms based on partial differential equation

    Institute of Scientific and Technical Information of China (English)

    武伟; 王宏志; 宋宇

    2011-01-01

    介绍了3种常见的偏微分方程去噪模型:热扩散方程、P-M扩散方程、TV扩散方程,并结合实验分析了它们在图像去噪中的优缺点。%Three image denoising models based on partial differential equation,hot diffusion equation,P-M diffusion equation and TV diffusion equation are introduced.By means of experimental analysis,the advantages and disadvantages are listed for image denoising.

  11. A fast and adaptive method for complex-valued S AR image denoising based on lk norm regularization

    Institute of Scientific and Technical Information of China (English)

    WANG WeiWei; WANG ZhengMing; YUAN ZhenYu; LI MingShan

    2009-01-01

    This paper developed a fast and adaptive method for SAR complex image denoising based on lk norm regularization, as viewed from parameters estimation. We firstly establish the relationship between denoising model and ill-posed inverse problem via convex half-quadratic regularization, and compare the difference between the estimator variance obtained from the iterative formula and biased CramerRao bound, which proves the theoretic flaw of the existent methods of parameter selection. Then, the analytic expression of the model solution as the function with respect to the regularization parameter is obtained. On this basis, we study the method for selecting the regularization parameter through minimizing mean-square error of estimators and obtain the final analytic expression, which resulted in the direct calculation, high processing speed, and adaptability. Finally, the effect of regularization parameter selection on the resolution of point targets is analyzed. The experiment results of simulation and real complex-valued SAR images illustrate the validity of the proposed method.

  12. Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

    Science.gov (United States)

    Zeng, Xianhua; Bian, Wei; Liu, Wei; Shen, Jialie; Tao, Dacheng

    2015-11-01

    Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.

  13. Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

    Science.gov (United States)

    Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

    2016-03-01

    In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

  14. Comparative Study of Image Denoising Algorithms in Digital Image Processing

    Directory of Open Access Journals (Sweden)

    Aarti

    2014-05-01

    Full Text Available This paper proposes a basic scheme for understanding the fundamentals of digital image processing and the image denising algorithm. There are three basic operation categorized on during image processing i.e. image rectification and restoration, enhancement and information extraction. Image denoising is the basic problem in digital image processing. The main task is to make the image free from Noise. Salt & pepper (Impulse noise and the additive white Gaussian noise and blurredness are the types of noise that occur during transmission and capturing. For denoising the image there are some algorithms which denoise the image.

  15. Comparative Study of Image Denoising Algorithms in Digital Image Processing

    Directory of Open Access Journals (Sweden)

    Aarti Kumari

    2015-11-01

    Full Text Available This paper proposes a basic scheme for understanding the fundamentals of digital image processing and the image denising algorithm. There are three basic operation categorized on during image processing i.e. image rectification and restoration, enhancement and information extraction. Image denoising is the basic problem in digital image processing. The main task is to make the image free from Noise. Salt & pepper (Impulse noise and the additive white Gaussian noise and blurredness are the types of noise that occur during transmission and capturing. For denoising the image there are some algorithms which denoise the image.

  16. Image denoising with the dual-tree complex wavelet transform

    Science.gov (United States)

    Yaseen, Alauldeen S.; Pavlova, Olga N.; Pavlov, Alexey N.; Hramov, Alexander E.

    2016-04-01

    The purpose of this study is to compare image denoising techniques based on real and complex wavelet-transforms. Possibilities provided by the classical discrete wavelet transform (DWT) with hard and soft thresholding are considered, and influences of the wavelet basis and image resizing are discussed. The quality of image denoising for the standard 2-D DWT and the dual-tree complex wavelet transform (DT-CWT) is studied. It is shown that DT-CWT outperforms 2-D DWT at the appropriate selection of the threshold level.

  17. Image denoising using least squares wavelet support vector machines

    Institute of Scientific and Technical Information of China (English)

    Guoping Zeng; Ruizhen Zhao

    2007-01-01

    We propose a new method for image denoising combining wavelet transform and support vector machines (SVMs). A new image filter operator based on the least squares wavelet support vector machines (LSWSVMs) is presented. Noisy image can be denoised through this filter operator and wavelet thresholding technique. Experimental results show that the proposed method is better than the existing SVM regression with the Gaussian radial basis function (RBF) and polynomial RBF. Meanwhile, it can achieve better performance than other traditional methods such as the average filter and median filter.

  18. Local sparse representation for astronomical image denoising

    Institute of Scientific and Technical Information of China (English)

    杨阿锋; 鲁敏; 滕书华; 孙即祥

    2013-01-01

    Motivated by local coordinate coding(LCC) theory in nonlinear manifold learning, a new image representation model called local sparse representation(LSR) for astronomical image denoising was proposed. Borrowing ideas from surrogate function and applying the iterative shrinkage-thresholding algorithm(ISTA), an iterative shrinkage operator for LSR was derived. Meanwhile, a fast approximated LSR method by first performing a K-nearest-neighbor search and then solving a l1optimization problem was presented under the guarantee of denoising performance. In addition, the LSR model and adaptive dictionary learning were incorporated into a unified optimization framework, which explicitly established the inner connection of them. Such processing allows us to simultaneously update sparse coding vectors and the dictionary by alternating optimization method. The experimental results show that the proposed method is superior to the traditional denoising method and reaches state-of-the-art performance on astronomical image.

  19. Denoising Of Ultrasonographic Images Using DTCWT

    Directory of Open Access Journals (Sweden)

    Anil Dudy

    2012-08-01

    Full Text Available Digital image acquisition and processing pays a very important role in current medical diagnosis techniques. Medical images are corrupted by noise in its acquisition and transmission process. Ultrasound has historically suffered from an inherent imaging artifact known as speckle. Speckle significantly degrades the image quality. It makes it more difficult for observer to discriminate fine details of the images in diagnostic examination. Dual tree complex wavelet transform is an efficient method for denoising of ultrasound images. It not only reduces the speckle noise but also preserves the detail features of image. In this paper denoising of ultrasound images has been performed using Dual tree complex wavelet transform. In experimental analysis, it is found that the performance in terms of PSNR for a set of acquired medical images brain and mammogram is better with DTCWT as compared to the performance with DWT.

  20. A Distortion Input Parameter in Image Denoising Algorithms with Wavelets

    Directory of Open Access Journals (Sweden)

    Anisia GOGU

    2009-07-01

    Full Text Available The problem of image denoising based on wavelets is considered. The paper proposes an image denoising method by imposing a distortion input parameter instead of threshold. The method has two algorithms. The first one is running off line and it is applied to the prototype of the image class and it building a specific dependency, linear or nonlinear, between the final desired distortion and the necessary probability of the details coefficients. The next algorithm, is directly applying the denoising with a threshold computed from the previous step. The threshold is estimated by using the probability density function of the details coefficients and by imposing the probability of the coefficients which will be kept. The obtained results are at the same quality level with other well known methods.

  1. 基于稀疏序列的图像去噪方法及应用%Image Denoising Based on Sparse Sequences and Its Application

    Institute of Scientific and Technical Information of China (English)

    王蓓; 张欣; 刘洪

    2011-01-01

    文中基于图像稀疏分解,根据图像与噪声的稀疏分解不同,提出一种基于非对称原子模型的原子库,通过算法优化,实现对采集的布坯图像进行有效去噪分析,提高去噪图像的PSNR值,且具有更好的视觉效果.将所采集到的布坯数字图像去噪后将背景和缺陷进行分离,才能更有效地将缺陷进行界定,以利后续的相关特征提取.通过实验,与小波类去噪方法对比,文中的学习算法能更好地去除图像噪声,保留图像细节信息,获得更高PSNB值.%Base on the image sparse decomposition,according to the different characters of image and noise in sparse decomposition, proposed a model based on asymmetric atomic atoms library ,by algorithm the acquisition of effective de-noising analysis of gray images.Denoising to improve image PSNR values, and has a better visual effect. Will be collected by digital image denoising cloth blank background and the defects after separation in order to more effectively define the defects in order to facilitate the follow-up of the relevant characteristics of extraction. Experimental results show that :in comparison with the wavelet based denoising methods,our learning based algorithm has better denoising ability, keep more detail image information and improve the peak signal to noise ratio.

  2. PDE-SVD Based Audio Denoising

    OpenAIRE

    Baravdish, George; Evangelista, Gianpaolo; Svensson, Olof; Sofya, Faten

    2012-01-01

    In this paper we present a new method for denoising audio signals. The method is based on the Singular Value Decomposition (SVD) of the frame matrix representing the signal inthe Overlap Add decomposition. Denoising is performed by modifying both the singular values, using a tapering model, and the singular vectors of the representation, using a nonlinear PDE method. The performance of the method is evaluated and compared with denoising obtained by filtering.

  3. An Image De-noising Algorithm Based on Redundance Removed Dictionary%一种基于去冗余字典的图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    张丹莹; 李翠华; 李雄宗; 施华; 张东晓

    2012-01-01

    图像去噪是图像处理中的关键问题之一,也是图像后续处理的基础.结合近年来兴起的稀疏表示理论,能更好的处理图像去噪问题.在正交匹配追踪(orthogonal matching pursuit,OMP)的基础上,采用K-奇异值分解(K-SVD)算法对图像进行去噪.为了得到更好的去噪效果,改进了字典更新算法,对字典原子进行优化选择,去除冗余的字典原子,并用图像块替换字典原子,用于提高字典训练的效率,与自然图像数据相适应.实验结果表明,与小波去噪算法相比,该算法具有良好的去噪能力,能较好地保持图像的细节和边缘特征,去噪后的图像更为清晰.%Image denoising is one of the key issues in the image processing and the foundation of further research. Combined with the sparse representation theory, which emerged in recent year,we can handle the image denoising problems better. Based on orthogonal matching pursuit(OMP) algorithm, this paper used K-singular value decomposition(K-SVD) algorithm for image de-noising. In order to get better de-noising performance, this paper improves dictionary updating algorithm. The core provides a more optimal choice for training of the dictionary atoms, replaces the useless and redundant dictionary of atoms with natural image patch dictionary atoms. By this way, we improve the training of the dictionary effectively, and adapt to natural image. Experimenlal results show that, compare with the wavelet de-noising algorithm, this algorithm has a good de-noising ability, while keeping the detail and the edge character of the image better, make the de-noising image clear.

  4. 基于聚类的图像稀疏去噪方法%Clustering based sparse model for image denoising

    Institute of Scientific and Technical Information of China (English)

    叶敏超; 钱沄涛; 沈言浩

    2011-01-01

    Non-local means and sparse models are two important denoising methods attracting many attentions during the recent years. The non-local means method uses the weighted average of the pixels that share similar neighborhoods as the denoised result, and the sparse denoising method recovers the non-noisy components of an image by a sparse representation with a few atoms in a dictionary. Based on these two denoising methods, we propose a clustering based sparse model for image denoising, which first partitions the image patches according to their similarities, and then uses e1/e2 norm regularization to make the similar image patches in the same cluster share the same sparse structure when they are represented by overcompleted dictionary. For dictionary selection, two dictionaries Discrete Cosin Transformation ( DCT) dictionary and bi-orthogonal wavelet dictionary are chosen to represent both smooth components and detail components of the image. Experiment results show that the proposed method has better performance of image denoising compared with some traditional sparse denoising methods.%在图像去噪方法的研究中,非局部均值算法与稀疏去噪算法是近几年受到广为关注的方法.非局部均值算法将具有邻域相似性的像素点作加权平均;而稀疏去噪算法是将图像的非噪声部分用过完备字典进行稀疏表示.基于上述两种方法的思想,本文提出了基于聚类的稀疏去噪方法,该方法结合了非局部均值算法与稀疏去噪算法的优点,对相似的图像块进行聚类,并通过施加l1/l2范数的正则化约束,对同一类中的图像块在过完备字典上进行相同结构的稀疏表示,从而达到去噪目的.在字典的选择上,本文使用DCT字典和双正交小波字典,能够同时保留原图像中的平滑分量与细节分量.实验结果表明,本文方法比传统的稀疏去噪方法有更好的去噪效果.

  5. 基于四叉树复小波的自适应图像去噪%Adaptive Image Denoising Based on Quadtree Complex Wavelet

    Institute of Scientific and Technical Information of China (English)

    杨国梁

    2012-01-01

    小波变换应用于图像去噪处理方面得到广泛的应用,但其自身也存在着一些缺点和不易控制因素,大大地限制了其图像去噪能力和应用的范围.而四叉树复小波变换很好地改善了这一缺点,具有较好的方向选择性和平移不变性,并且容易实现完全重构.本文通过对常见的图像去噪方法,对其原理进行解析,再讲解基于四叉树复小波的自适应图像去噪的优点和发展趋势,让大家对现代复小波自适应图像去噪有一个清晰的认识.%The wavelet transform has been widely used in image denoising, but there are also some shortcomings and difficult to control factors, which greatly limits its image denoising capabilities and range of applications. Quad-tree complex wavelet transform improves the shortcomings, and it has better direction selectivity and translational invariance, and it is easy to achieve perfect reconstruction. Through the analysis of the principle of common image denoising, this paper explains the advantages and development trends of adaptive image denoising based on the quad-tree complex wavelet, so that we have a clear understanding on modern complex wavelet adaptive image denoising.

  6. Simultaneous denoising and compression of multispectral images

    Science.gov (United States)

    Hagag, Ahmed; Amin, Mohamed; Abd El-Samie, Fathi E.

    2013-01-01

    A new technique for denoising and compression of multispectral satellite images to remove the effect of noise on the compression process is presented. One type of multispectral images has been considered: Landsat Enhanced Thematic Mapper Plus. The discrete wavelet transform (DWT), the dual-tree DWT, and a simple Huffman coder are used in the compression process. Simulation results show that the proposed technique is more effective than other traditional compression-only techniques.

  7. Robust Image Denoising using a Virtual Flash Image for Monte Carlo Ray Tracing

    DEFF Research Database (Denmark)

    Moon, Bochang; Jun, Jong Yun; Lee, JongHyeob

    2013-01-01

    parameters. To highlight the benefits of our method, we apply our method to two Monte Carlo ray tracing methods, photon mapping and path tracing, with various input scenes. We demonstrate that using virtual flash images and homogeneous pixels with a standard denoising method outperforms state-of-the-art......We propose an efficient and robust image-space denoising method for noisy images generated by Monte Carlo ray tracing methods. Our method is based on two new concepts: virtual flash images and homogeneous pixels. Inspired by recent developments in flash photography, virtual flash images emulate...... values. While denoising each pixel, we consider only homogeneous pixels—pixels that are statistically equivalent to each other. This makes it possible to define a stochastic error bound of our method, and this bound goes to zero as the number of ray samples goes to infinity, irrespective of denoising...

  8. 基于单小波和多小波的红外图像盲去噪%Infrared Image Denoising Based on Single-wavelet and Multiwavelets

    Institute of Scientific and Technical Information of China (English)

    费佩燕; 郭宝龙

    2005-01-01

    Deviation is essential to classic soft threshold denoising in wavelet domain. Texture features of noised image denoised by wavelet transform were weakened. Gibbs effect is distinct at edges of image.Image blurs comparing with original noised image. To solve the questions, a blind denoising method based on single-wavelet transform and multiwavelets transform was proposed. The method doesn't depend on size of image and deviation to determine threshold of wavelet coefficients, which is different from classical soft-threshold denoising in wavelet domain. Moreover, the method is good for many types of noise. Gibbs effect disappeared with this method, edges of image are preserved well, and noise is smoothed and restrained effectively.

  9. Survey Paper on Image Denoising Using Spatial Statistic son Pixel

    Directory of Open Access Journals (Sweden)

    Varun Nigam

    2015-01-01

    Full Text Available The classical non-local means image denoising approach, the value of a pixel is determined based on the weighted average of other pixels, where the weights are determined based on a fixed isotropic ally weighted similarity function between the local neighbourhoods. It is demonstrate that noticeably improved perceptual quality can be achieved through the use of adaptive anisotropic ally weighted similarity functions between local neighbourhoods. This is accomplished by adapting the similarity weighing function in an anisotropic manner based on the perceptual characteristics of the underlying image content derived efficiently based on the Mexican Hat wavelet. Experimental results show that the it can be used to provide improved perceptual quality in the denoised image both quantitatively and qualitatively when compared to existing methods.

  10. The Noise Clinic: a Blind Image Denoising Algorithm

    Directory of Open Access Journals (Sweden)

    Marc Lebrun

    2015-01-01

    Full Text Available This paper describes the complete implementation of a blind image algorithm, that takes any digital image as input. In a first step the algorithm estimates a Signal and Frequency Dependent (SFD noise model. In a second step, the image is denoised by a multiscale adaptation of the Non-local Bayes denoising method. We focus here on a careful analysis of the denoising step and present a detailed discussion of the influence of its parameters. Extensive commented tests of the blind denoising algorithm are presented, on real JPEG images and scans of old photographs.

  11. Application of multi-resolution analysis in sonar image denoising

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform (FRIT) deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.

  12. PDE-based Non-Linear Diffusion Techniques for Denoising Scientific and Industrial Images: An Empirical Study

    Energy Technology Data Exchange (ETDEWEB)

    Weeratunga, S K; Kamath, C

    2001-12-20

    Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, they focus on one such approach, namely the use of non-linear diffusion operators. This approach has been studied extensively from a theoretical viewpoint ever since the 1987 work of Perona and Malik showed that non-linear filters outperformed the more traditional linear Canny edge detector. They complement this theoretical work by investigating the performance of several isotropic diffusion operators on test images from scientific domains. They explore the effects of various parameters such as the choice of diffusivity function, explicit and implicit methods for the discretization of the PDE, and approaches for the spatial discretization of the non-linear operator etc. They also compare these schemes with simple spatial filters and the more complex wavelet-based shrinkage techniques. The empirical results show that, with an appropriate choice of parameters, diffusion-based schemes can be as effective as competitive techniques.

  13. Developing an efficient technique for satellite image denoising and resolution enhancement for improving classification accuracy

    Science.gov (United States)

    Thangaswamy, Sree Sharmila; Kadarkarai, Ramar; Thangaswamy, Sree Renga Raja

    2013-01-01

    Satellite images are corrupted by noise during image acquisition and transmission. The removal of noise from the image by attenuating the high-frequency image components removes important details as well. In order to retain the useful information, improve the visual appearance, and accurately classify an image, an effective denoising technique is required. We discuss three important steps such as image denoising, resolution enhancement, and classification for improving accuracy in a noisy image. An effective denoising technique, hybrid directional lifting, is proposed to retain the important details of the images and improve visual appearance. The discrete wavelet transform based interpolation is developed for enhancing the resolution of the denoised image. The image is then classified using a support vector machine, which is superior to other neural network classifiers. The quantitative performance measures such as peak signal to noise ratio and classification accuracy show the significance of the proposed techniques.

  14. Denoising human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation

    Energy Technology Data Exchange (ETDEWEB)

    Bao, L J; Zhu, Y M; Liu, W Y; Pu, Z B; Magnin, I E [HIT-INSA Sino French Research Centre for Biomedical Imaging, Harbin Institute of Technology, Harbin (China); Croisille, P; Robini, M [CREATIS-LRMN, CNRS UMR 5220, Inserm U630, INSA of Lyon, University of Lyon 1, Villeurbanne (France)], E-mail: baolij@gmail.com

    2009-03-21

    Cardiac diffusion tensor magnetic resonance imaging (DT-MRI) is noise sensitive, and the noise can induce numerous systematic errors in subsequent parameter calculations. This paper proposes a sparse representation-based method for denoising cardiac DT-MRI images. The method first generates a dictionary of multiple bases according to the features of the observed image. A segmentation algorithm based on nonstationary degree detector is then introduced to make the selection of atoms in the dictionary adapted to the image's features. The denoising is achieved by gradually approximating the underlying image using the atoms selected from the generated dictionary. The results on both simulated image and real cardiac DT-MRI images from ex vivo human hearts show that the proposed denoising method performs better than conventional denoising techniques by preserving image contrast and fine structures.

  15. ORTHOGONAL-DIRECTIONAL FORWARD DIFFUSION IMAGE INPAINTING AND DENOISING MODEL

    Institute of Scientific and Technical Information of China (English)

    Wu Jiying; Ruan Qiuqi; An Gaoyun

    2008-01-01

    In this paper,an orthogonal-directional forward diffusion Partial Differential Equation (PDE) image inpainting and denoising model which processes image based on variation problem is proposed. The novel model restores the damaged information and smoothes the noise in image si-multaneously. The model is morphological invariant which processes image based on the geometrical property. The regularization item of it diffuses along and cross the isophote,and then the known image information is transported into the target region through two orthogonal directions. The cross isophote diffusion part is the TV (Total Variation) equation and the along isophote diffusion part is the inviscid Helmholtz vorticity equation. The equivalence between the Helmholtz equation and the inpainting PDEs is proved. The model with the fidelity item which is used in the whole image domain denoises while preserving edges. So the novel model could inpaint and denoise simultaneously. Both theoretical analysis and experiments have verified the validity of the novel model proposed in this paper.

  16. GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

    Institute of Scientific and Technical Information of China (English)

    Li Xiangping; Wang Xiaotian; Shi Guangming

    2011-01-01

    NonLocal Means (NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis (PCA),Principle Neighborhood Dictionary (PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preprocessing process,the principle components computed are more accurate resulting in an improved denoising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio (PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

  17. Image Denoising Algorithm Based on Nonlocally Sparse Representation and Group%组约束与非局部稀疏的图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    陈利霞; 赛朋飞

    2015-01-01

    The most existing denoising algorithms based on nonlocal sparse representation are strictly dependent on patch matching ,and the denoising performance is subject to the numbers of similar patches .So a image denoising algorithm based on nonlocally sparse representation and group is proposed . The group‐based constraints is introduced to the nonlocal sparse representation ,which can enhance the nonlocal similarity between image patches and the patch matching is more accurate .Experiments show that the model has a good performance in both visual effect and peak signal to noise ratio .%现有的非局部稀疏表示去噪算法大多严格依赖于块匹配,且其去噪性能受制于匹配的相似块的数量。鉴于此,提出了组约束与非局部稀疏的图像去噪模型。模型在非局部稀疏的基础上加入了分组约束,增强了图像块之间的非局部相似度,块匹配更加精确。实验表明,模型无论是在视觉效果还是峰值信噪比上均具有较好的性能。

  18. A Fast Wavelet Multilevel Approach to Total Variation Image Denoising

    Directory of Open Access Journals (Sweden)

    Kossi Edoh

    2009-09-01

    Full Text Available In this paper we present an adaptive multilevel total variational (TV method for image denoising which utilizes TV partial differential equation (PDE models and exploits the multiresolution properties of wavelets. The adaptive multilevel TV method provides fast adaptive wavelet-based solvers for the TV model. Our approach employs a wavelet collocation method applied to the TV model using two-dimensional anisotropic tensor product of Daubechies wavelets. The algorithm inherently combines the denoising property of wavelet compression algorithms with that of the TV model, and produces results superior to each method when implemented alone. It exploits the edge preservation property of the TVmodel to reduce the oscillations that may be generated around the edges in wavelet compression. In contrast with previous work combining TV denoising with wavelet compression, the method presented in this paper treats the numerical solution in a novel waywhich decreases the computational cost associated with the solution of the TV model. We present a detailed description of our method and results which indicate that a combination of wavelet based denoising techniques with the TV model produces superior results, for afraction of the computational cost.

  19. A Comparative Study of Wavelet Thresholding for Image Denoising

    Directory of Open Access Journals (Sweden)

    Arun Dixit

    2014-11-01

    Full Text Available Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps – first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.

  20. A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

    Directory of Open Access Journals (Sweden)

    Zhang Liangpei

    2011-01-01

    Full Text Available Abstract This work presents a scale-based forward-and-backward diffusion (SFABD scheme. The main idea of this scheme is to perform local adaptive diffusion using local scale information. To this end, we propose a diffusivity function based on the Minimum Reliable Scale (MRS of Elder and Zucker (IEEE Trans. Pattern Anal. Mach. Intell. 20(7, 699-716, 1998 to detect the details of local structures. The magnitude of the diffusion coefficient at each pixel is determined by taking into account the local property of the image through the scales. A scale-based variable weight is incorporated into the diffusivity function for balancing the forward and backward diffusion. Furthermore, as numerical scheme, we propose a modification of the Perona-Malik scheme (IEEE Trans. Pattern Anal. Mach. Intell. 12(7, 629-639, 1990 by incorporating edge orientations. The article describes the main principles of our method and illustrates image enhancement results on a set of standard images as well as simulated medical images, together with qualitative and quantitative comparisons with a variety of anisotropic diffusion schemes.

  1. Oriented wavelet transform for image compression and denoising.

    Science.gov (United States)

    Chappelier, Vivien; Guillemot, Christine

    2006-10-01

    In this paper, we introduce a new transform for image processing, based on wavelets and the lifting paradigm. The lifting steps of a unidimensional wavelet are applied along a local orientation defined on a quincunx sampling grid. To maximize energy compaction, the orientation minimizing the prediction error is chosen adaptively. A fine-grained multiscale analysis is provided by iterating the decomposition on the low-frequency band. In the context of image compression, the multiresolution orientation map is coded using a quad tree. The rate allocation between the orientation map and wavelet coefficients is jointly optimized in a rate-distortion sense. For image denoising, a Markov model is used to extract the orientations from the noisy image. As long as the map is sufficiently homogeneous, interesting properties of the original wavelet are preserved such as regularity and orthogonality. Perfect reconstruction is ensured by the reversibility of the lifting scheme. The mutual information between the wavelet coefficients is studied and compared to the one observed with a separable wavelet transform. The rate-distortion performance of this new transform is evaluated for image coding using state-of-the-art subband coders. Its performance in a denoising application is also assessed against the performance obtained with other transforms or denoising methods.

  2. Denoising for finger vein image based on sparse decomposition%基于稀疏分解的指静脉图像去噪

    Institute of Scientific and Technical Information of China (English)

    刘洋; 郭树旭; 张凤春; 李扬

    2012-01-01

    Finger vein recognition had attracted great attention for its unique advantages. For the finger vein images we obtained from the hardware system often contain severe noise, shadow and other issues, denoising of the low quality images had become the key to the recognition process. For this reason, a new image denoising method based sparse representation for finger vein image was proposed in this paper. Image denoising method based on image sparse decomposition is different from the traditional image denoising methods. In this method, image corrupted by noise is decomposed into two parts. One part is the image sparse components which are related to image information. Another part, which remains after the image sparse components are subtracted from the image, is regarded as noise. Image can be denoised by reconstructing image only with its sparse components. The over-complete dictionary with the vein features of finger vein images was constructed by u-sing the Gaussian function. The performance of the new method was verified by both synthetic and real finger vein images. Experimental results show that this algorithm can get better PSNR 1-2 dB compared with the traditional denoising.%手指静脉识别技术因其独特的优势,受到广泛的关注.然而由硬件系统获取的手指静脉图像常常含有严重的噪声、阴影等问题,所以对低质量的静脉图像的去噪成为了整个识别过程的关键.本文提出了一种基于稀疏分解的指静脉图像去噪新方法.基于稀疏分解的图像去噪是将含有噪声的图像信息进行稀疏分解,分解成稀疏成分和其他成分.其中的稀疏部分是有用信息,其他部分被认为是噪声,再由图像的稀疏部分重建原始信号,达到恢 复原始信号并去除噪声的效果.本文根据指静脉图像的静脉的特点,应用高斯函数构造了过完备库.用合成图像和真实指静脉图像分别对新算法进行实验验证.实验结果证明,与

  3. Scalar Parameters Optimization in PDE Based Medical Image Denoising by using Cellular Wave Computing

    Directory of Open Access Journals (Sweden)

    GACSÁDI Alexandru

    2016-10-01

    Full Text Available In order to help with biomedical images, a set of complex and effective mathematical models are available, based on the PDE (PDE - partial differential equation. On one hand, effective implementation of these methods is difficult, due to the difficulty of determining the scalar parameter values, on which the image processing efficiency depends, while on the other hand, due to the considerable computing power needed in order to perform in real time. Currently there are no analytical and / or experimental methods in the literature for the exact values determination of the scaled parameters to provide the best results for a specific image processing. This paper proposes a method for optimizing the values of a scaling parameter set, which ensure effective noise reduction of medical images by using cellular wave computing. To assess the overall performance of noise extraction, the error function (quantitative component and direct visualization (qualitative component are used at the same time. Moreover, by using this analysis, the degree to which the CNN templates are robust against the range of values of the scalar parameter, is obtainable.

  4. Wavelet threshold image denoising algorithm based on MATLAB different wavelet bases%基于MATLAB不同小波基的小波阈值图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    曾敬枫

    2016-01-01

    Through the introduction of wavelet image denoising method and wavelet threshold denoising steps,this paper discusses the role of wavelet bases in wavelet threshold denoising, and describes the characteristics of several common wavelet bases and their correlation properties. Finally, respectively with a db2 and sym4 two kinds of wavelet bases by MATLAB, to denoise wavelet threshold realizes the image filtering and reconstruction of high frequency coefficients, so the conclusion is obtained that using different wavelet bases affects the results of image denoising.%通过介绍小波图像去噪的方法和小波阈值去噪的步骤,讨论小波基在小波阈值去噪中的作用,阐述了常见的几种小波基的特征及其相关性质的比较。最后通过在MATLAB下,分别选择了db2和sym4两种小波基,进行小波阈值去噪实现图像高频系数的滤波并重建,得到采用不同的小波基影响图像去噪效果的结论。

  5. Window shrink contourlet coefficients for image denoising

    Institute of Scientific and Technical Information of China (English)

    JIN Wei; PAN Ying-jun; WEI Biao; FENG Peng

    2005-01-01

    An adaptive image denosing technique was proposed to achieve the tradeoff between details retain and noises removal. In order to achieve this objective, the contourlet transform was introduced and a new threshold method, namely CWinShrink, is presented. It shrinks the contourlet coefficients with adaptive shrinkage factors. The shrinkage factors were calculated with reference to the sum of squares of the contourlet coefficients within the neighborhood window. This approach achieves enhanced results for images those are corrupted with additive Gaussian noise. In numerical comparisons with various methods, for a set of noisy images ( the PSNR range from 10.86dB to 26.91dB) , the presented method outperforms VisuShrink and Wiener filter in terms of the PSNR. Experiments also show that this method not only keeps the details of image but also yields denoised images with better visual quality.

  6. PERFORMANCE ANALYSIS OF IMAGE DENOISING WITH WAVELET THRESHOLDING METHODS FOR DIFFERENT LEVELS OF DECOMPOSITION

    Directory of Open Access Journals (Sweden)

    Anutam

    2014-10-01

    Full Text Available Image Denoising is an important part of diverse image processing and computer vision problems. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. One of the most powerful and perspective approaches in this area is image denoising using discrete wavelet transform (DWT. In this paper, comparison of various Wavelets at different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio (PSNR of image gets decreased whereas Mean Absolute Error (MAE and Mean Square Error (MSE get increased . A comparison of filters and various wavelet based methods has also been carried out to denoise the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other methods.

  7. Denoising two-photon calcium imaging data.

    Science.gov (United States)

    Malik, Wasim Q; Schummers, James; Sur, Mriganka; Brown, Emery N

    2011-01-01

    Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.

  8. A new method for mobile phone image denoising

    Science.gov (United States)

    Jin, Lianghai; Jin, Min; Li, Xiang; Xu, Xiangyang

    2015-12-01

    Images captured by mobile phone cameras via pipeline processing usually contain various kinds of noises, especially granular noise with different shapes and sizes in both luminance and chrominance channels. In chrominance channels, noise is closely related to image brightness. To improve image quality, this paper presents a new method to denoise such mobile phone images. The proposed scheme converts the noisy RGB image to luminance and chrominance images, which are then denoised by a common filtering framework. The common filtering framework processes a noisy pixel by first excluding the neighborhood pixels that significantly deviate from the (vector) median and then utilizing the other neighborhood pixels to restore the current pixel. In the framework, the strength of chrominance image denoising is controlled by image brightness. The experimental results show that the proposed method obviously outperforms some other representative denoising methods in terms of both objective measure and visual evaluation.

  9. Total-variation-based methods for gravitational wave denoising

    CERN Document Server

    Torres, Alejandro; Font, José A; Ibáñez, José M

    2014-01-01

    We describe new methods for denoising and detection of gravitational waves embedded in additive Gaussian noise. The methods are based on Total Variation denoising algorithms. These algorithms, which do not need any a priori information about the signals, have been originally developed and fully tested in the context of image processing. To illustrate the capabilities of our methods we apply them to two different types of numerically-simulated gravitational wave signals, namely bursts produced from the core collapse of rotating stars and waveforms from binary black hole mergers. We explore the parameter space of the methods to find the set of values best suited for denoising gravitational wave signals under different conditions such as waveform type and signal-to-noise ratio. Our results show that noise from gravitational wave signals can be successfully removed with our techniques, irrespective of the signal morphology or astrophysical origin. We also combine our methods with spectrograms and show how those c...

  10. Research on image denoising based on time-space fractional partial differential equations%基于时间-空间分数阶偏微分方程的图像去噪模型

    Institute of Scientific and Technical Information of China (English)

    黄果; 许黎; 陈庆利; 蒲亦非

    2012-01-01

    In order to preserve more image details information while image denoising, the concept of fractional-order gradient descent flow is proposed by combining fractional calculus and gradient descent flow, and the fractional-order gradient descent flow of an energy function is convergent within a certain range of differential order. On this base, the denoising model based on time-space fractional partial equations is constructed by adding a time factor to the improved denoising model based on space fractional partial equations. The proposed denoising model can be implemented to remove noise at the time and space direction simultaneously. The experimental results show that, compared with the existing denoising model, the improved image denoising model based on time-space fractional partial differential equations could make the visual effect better and has a faster computing speed. In addition, compared with the image denoising model based on space fractional partial differential equations, the image denoising model based on time-space fractional partial differential equations can appropriately increase the signal-to-noise ratio of images and significantly reduce the iteration number under the condition that the signal-to-noise ratio of the denoising image getting the maximum.%为了在去噪的同时更多地保留图像的细节信息,将分数阶微积分理论和梯度下降流有效结合,提出了分数阶梯度下降流的概念,并证明了能量泛函的分数阶梯度下降流在一定微分阶次范围内是收敛的.在此基础上,将时间因素引入到改进的基于空间分数阶偏微分方程的去噪模型中,从而构建了基于时间-空间分数阶偏微分方程的去噪模型,该模型实现了在时间方向上和空间平面内的同时去噪.实验结果表明,提出的基于时间-空间分数阶偏微分方程的图像去噪模型较基于空间分数阶偏微分方程的图像去噪模型不仅可以提高信噪比,而且可以大幅减

  11. Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

    Science.gov (United States)

    Kittisuwan, Pichid

    2015-03-01

    The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

  12. Evaluating image denoising methods in myocardial perfusion single photon emission computed tomography (SPECT) imaging

    Science.gov (United States)

    Skiadopoulos, S.; Karatrantou, A.; Korfiatis, P.; Costaridou, L.; Vassilakos, P.; Apostolopoulos, D.; Panayiotakis, G.

    2009-10-01

    The statistical nature of single photon emission computed tomography (SPECT) imaging, due to the Poisson noise effect, results in the degradation of image quality, especially in the case of lesions of low signal-to-noise ratio (SNR). A variety of well-established single-scale denoising methods applied on projection raw images have been incorporated in SPECT imaging applications, while multi-scale denoising methods with promising performance have been proposed. In this paper, a comparative evaluation study is performed between a multi-scale platelet denoising method and the well-established Butterworth filter applied as a pre- and post-processing step on images reconstructed without and/or with attenuation correction. Quantitative evaluation was carried out employing (i) a cardiac phantom containing two different size cold defects, utilized in two experiments conducted to simulate conditions without and with photon attenuation from myocardial surrounding tissue and (ii) a pilot-verified clinical dataset of 15 patients with ischemic defects. Image noise, defect contrast, SNR and defect contrast-to-noise ratio (CNR) metrics were computed for both phantom and patient defects. In addition, an observer preference study was carried out for the clinical dataset, based on rankings from two nuclear medicine clinicians. Without photon attenuation conditions, denoising by platelet and Butterworth post-processing methods outperformed Butterworth pre-processing for large size defects, while for small size defects, as well as with photon attenuation conditions, all methods have demonstrated similar denoising performance. Under both attenuation conditions, the platelet method showed improved performance with respect to defect contrast, SNR and defect CNR in the case of images reconstructed without attenuation correction, however not statistically significant (p > 0.05). Quantitative as well as preference results obtained from clinical data showed similar performance of the

  13. A Image Denoising Method Based on Similar Image Retrieval and Dictionary Learning%基于相似图像检索与字典学习的图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    胡占强; 耿龙

    2016-01-01

    In order to analyze and understand the image effectively, it's necessary to conduct denoising for image. Proposes a denoising method based on similar image retrieval and dictionary learning. Firstly, to have the better accuracy of image retrieval by improving noise signal ratio, denoising initially is executed for noise image; secondly, carry on image retrieval based on SIFT feature by using the initial noise image in the picture library and regard the similar image as a dictionary learning samples matched to improve correlation of dictionary and noise image; finally, the compensation of high frequency is needed. Satellite images are used to demonstrate the superiority of the proposed algorithm. Compared with the traditional denoising methods,the proposed method obtains better denoising effect,furthermore,it can effectively suppress the loss of high frequency information caused by the denoising procession.%为了更好地分析与理解图像,需对图像进行去噪。提出一种基于相似图像检索与字典学习的图像去噪方法。首先,为了提高图像检索的准确度,对噪声图像进行初始去噪提高信噪比;然后使用初始去噪图像在图片库里进行基于SIFT特征的图像检索,使用匹配到的相似图像作为字典学习的样本,提高字典与噪声图像的相关性;最后进行高频补偿。卫星图像被用于去噪实验证明所提算法的优越性。与传统去噪方法相比,所提出的方法不仅获得较好的去噪效果,而且在一定程度上有效地抑制去噪带来的高频信息丢失。

  14. Improved PDE image denoising method based on logarithmic image processing%改进的LIP偏微分方程图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    郭茂银; 田有先

    2011-01-01

    Concerning the defects of Logarithmic Image Processing-Total Variation (LIP_TV) denoising model, an improved Partial Differential Equation (PDE) image denoising method based on LIP was proposed.Based on LIP mathematic theory, the new LIP gradient operator was obtained by introducing four directional derivatives in the original one, which can control the diffusion process effectively because it measures image information comprehensively and objectively.The fidelity coefficient was constructed by adopting the noise visibility function based on the structure characteristic of human visual system, which can further preserve the edge details and avoid estimating noise level faetitionsly.The theoretical analysis and experimental results show that the improved method has superiority in the visual effect and objective quality, which can better remove noise and preserve detailed edge features.%针对对数图像处理-全变分(LIP-TV)去噪模型存在的不足,提出一种改进的LIP偏微分方程去噪方法.首先基于LIP数学理论,在LIP梯度算子中,引入四方向导数信息,得到改进的LIP梯度算子以全面客观地度量图像信息,更好地控制扩散过程.然后利用人类视觉系统的结构化特性,用噪声可见度函数构造新的保真项系数,进一步保持了图像的边缘细节并避免了人为估计噪声水平.理论分析和实验结果表明,该改进方法能够更好地去除噪声和保持图像边缘细节特征,在视觉效果和客观评价指标上都明显优于LIP-TV方法.

  15. Image denoising of PDE based on difference curvature%基于差分曲率的偏微分方程图像降噪算法

    Institute of Scientific and Technical Information of China (English)

    董婵婵; 张权; 郝慧艳; 张芳; 刘祎; 孙未雅; 桂志国

    2015-01-01

    针对图像去噪过程存在边缘保持与噪声抑制的矛盾,提出一种改进的偏微分方程图像降噪算法。将差分曲率引入各项异性扩散方程中,构造出新的扩散系数函数,其可以较好区分图像的边缘、平坦区域以及噪声,使算法在去除噪声的同时,保留更多弱边缘和纹理等细节信息。实验结果表明,该算法与其它基于偏微分方程的图像降噪算法相比,在客观的质量评价方面,提高了图像的信噪比和峰值信噪比;在主观视觉效果方面,在滤除噪声的同时保留了更多的弱边缘等细节信息,该算法实现了较好的图像降噪效果。%Concerning the contradiction between edge‐preserving and noise‐suppressing in the process of image denoising ,an im‐proved partial differential equation (PDE) for image denoising was proposed .Difference curvature was combined with anisotropic diffusion equation ,and a new diffusion coefficient function was constructed .As the new diffusion coefficient function effectively distinguished edges from ramp regions and isolated noise ,the model preserved more details such as weak edges and textures while getting rid of the noise .Experimental results show that ,compared with other image denoising methods based on PDE ,the proposed method improves signal‐to‐noise ratio (SNR) and peak‐signal‐to‐noise ratio (PSNR) in the terms of objective quality evaluation ,and preserves more detail information such as weak edges while removing the noise on the aspect of subjective visual effect .Therefore ,the presented algorithm obtains better image denoising results .

  16. Exploiting the self-similarity in ERP images by nonlocal means for single-trial denoising.

    Science.gov (United States)

    Strauss, Daniel J; Teuber, Tanja; Steidl, Gabriele; Corona-Strauss, Farah I

    2013-07-01

    Event related potentials (ERPs) represent a noninvasive and widely available means to analyze neural correlates of sensory and cognitive processing. Recent developments in neural and cognitive engineering proposed completely new application fields of this well-established measurement technique when using an advanced single-trial processing. We have recently shown that 2-D diffusion filtering methods from image processing can be used for the denoising of ERP single-trials in matrix representations, also called ERP images. In contrast to conventional 1-D transient ERP denoising techniques, the 2-D restoration of ERP images allows for an integration of regularities over multiple stimulations into the denoising process. Advanced anisotropic image restoration methods may require directional information for the ERP denoising process. This is especially true if there is a lack of a priori knowledge about possible traces in ERP images. However due to the use of event related experimental paradigms, ERP images are characterized by a high degree of self-similarity over the individual trials. In this paper, we propose the simple and easy to apply nonlocal means method for ERP image denoising in order to exploit this self-similarity rather than focusing on the edge-based extraction of directional information. Using measured and simulated ERP data, we compare our method to conventional approaches in ERP denoising. It is concluded that the self-similarity in ERP images can be exploited for single-trial ERP denoising by the proposed approach. This method might be promising for a variety of evoked and event-related potential applications, including nonstationary paradigms such as changing exogeneous stimulus characteristics or endogenous states during the experiment. As presented, the proposed approach is for the a posteriori denoising of single-trial sequences.

  17. Circular-Vector Based Non-Local SAR Image Denoising Algorithm%环形向量非局部SAR图像降噪算法

    Institute of Scientific and Technical Information of China (English)

    蔡雨辰; 赵保军; 唐林波

    2012-01-01

    提出一种基于环形向量的非局部SAR图像降噪算法.根据像素点的主方向提取环形向量,计算环形向量各自的特征向量.基于特征向量计算相似度权重,该方法的时间复杂度明显优于NL-Means的矩形模板匹配算法,且相似点匹配具有旋转不变性.通过仿真实验验证了该算法的计算速度和旋转不变性能,匹配效果明显优于NL-Means,降噪结果的峰值信噪比和结构相似度优于BM3D、BLS-GSM等主流降噪算法.%A circular-vector based non-local SAR image denoising algorithm was proposed. Circular vectors were abstracted according to the main direction of the central pixel. Feature vectors were then calculated and the weights of similarity were obtained based on these feature vectors. The execution time and quality of rotation invariance were tested. The results show that the time complexity is significantly lower than the patch-based similarity matching algorithm in NL-Means, and the rotation invariance is also maintained. Experiments have verified that the proposed algorithm shows better similarity matching results comparing to NL-Means and competitive denoising results on PSNR and SSIM against currently state-of-the-art denoising algorithms, such as BM3D, BLS-GSM.

  18. Wavelet-based denoising using local Laplace prior

    Science.gov (United States)

    Rabbani, Hossein; Vafadust, Mansur; Selesnick, Ivan

    2007-09-01

    Although wavelet-based image denoising is a powerful tool for image processing applications, relatively few publications have addressed so far wavelet-based video denoising. The main reason is that the standard 3-D data transforms do not provide useful representations with good energy compaction property, for most video data. For example, the multi-dimensional standard separable discrete wavelet transform (M-D DWT) mixes orientations and motions in its subbands, and produces the checkerboard artifacts. So, instead of M-D DWT, usually oriented transforms suchas multi-dimensional complex wavelet transform (M-D DCWT) are proposed for video processing. In this paper we use a Laplace distribution with local variance to model the statistical properties of noise-free wavelet coefficients. This distribution is able to simultaneously model the heavy-tailed and intrascale dependency properties of wavelets. Using this model, simple shrinkage functions are obtained employing maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimators. These shrinkage functions are proposed for video denoising in DCWT domain. The simulation results shows that this simple denoising method has impressive performance visually and quantitatively.

  19. A Total Variation Model Based on the Strictly Convex Modification for Image Denoising

    Directory of Open Access Journals (Sweden)

    Boying Wu

    2014-01-01

    Full Text Available We propose a strictly convex functional in which the regular term consists of the total variation term and an adaptive logarithm based convex modification term. We prove the existence and uniqueness of the minimizer for the proposed variational problem. The existence, uniqueness, and long-time behavior of the solution of the associated evolution system is also established. Finally, we present experimental results to illustrate the effectiveness of the model in noise reduction, and a comparison is made in relation to the more classical methods of the traditional total variation (TV, the Perona-Malik (PM, and the more recent D-α-PM method. Additional distinction from the other methods is that the parameters, for manual manipulation, in the proposed algorithm are reduced to basically only one.

  20. Multiwavelet Image Denoising Method Based on Singular Value Decomposition%基于奇异值分解的多小波图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    周广涛

    2013-01-01

    A multiwavelet image denoising method based on singular value decomposition (SVD) is put forward.The orthogonality and symmetry can be satisfied by multiwavelet transform at the same time which single wavelet could not.So multiwavelet transform is used to get the high frequency coefficient matrix.With the SVD,the signal features which drowned in the noise are extracted effectively.Finally denoised image is gotten through the reconstruction of multiwavelet transform.The simulation results show that noise is effectively removed,getting a good subjective visual effect.%给出了一种将多小波变换和奇异值分解相结合的图像去噪方法.该方法通过对含噪图像进行多小波变换,克服了单小波变换中无法同时满足正交性和对称性的缺点.对变换得到的高频系数矩阵进行奇异值分解去噪,提取高频系数中淹没在噪声中的信号成分,然后进行多小波重构,得到去噪图像.仿真结果表明,该方法能有效去除噪声,并获得良好的主观视觉效果.

  1. Quadtree structured image approximation for denoising and interpolation.

    Science.gov (United States)

    Scholefield, Adam; Dragotti, Pier Luigi

    2014-03-01

    The success of many image restoration algorithms is often due to their ability to sparsely describe the original signal. Shukla proposed a compression algorithm, based on a sparse quadtree decomposition model, which could optimally represent piecewise polynomial images. In this paper, we adapt this model to the image restoration by changing the rate-distortion penalty to a description-length penalty. In addition, one of the major drawbacks of this type of approximation is the computational complexity required to find a suitable subspace for each node of the quadtree. We address this issue by searching for a suitable subspace much more efficiently using the mathematics of updating matrix factorisations. Algorithms are developed to tackle denoising and interpolation. Simulation results indicate that we beat state of the art results when the original signal is in the model (e.g., depth images) and are competitive for natural images when the degradation is high.

  2. Denoising MR spectroscopic imaging data with low-rank approximations.

    Science.gov (United States)

    Nguyen, Hien M; Peng, Xi; Do, Minh N; Liang, Zhi-Pei

    2013-01-01

    This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singular value decomposition (SVD). The proposed method has been validated using simulated and experimental data, producing encouraging results. Specifically, the method can effectively denoise MRSI data in a wide range of SNR values while preserving spatial-spectral features. The method could prove useful for denoising MRSI data and other spatial-spectral and spatial-temporal imaging data as well.

  3. Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

    OpenAIRE

    Nguyen, Hien M.; Peng, Xi; Do, Minh N.; Liang, Zhi-Pei

    2012-01-01

    This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in in appropriate matrix forms (i.e., Casorati and Hankel) and applying low-rank approximations by singula...

  4. Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding

    Directory of Open Access Journals (Sweden)

    Alka Vishwa

    2012-06-01

    Full Text Available Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipment are producing images of different organs, which are used during various stages of analysis. Examples of such equipment include MRI, CT, ultrasound and X-Ray. In medical image processing, image denoising has become a very essential exercise all through the diagnosis as Ultrasound images are normally affected by speckle noise. The noise in the image has two negative outcomes, the first being the degradation of the image quality and the second and more important, obscures important information required for accurate diagnosis.Arbitration between the perpetuation of useful diagnostic information and noise suppression must be treasured in medical images. In general we rely on the intervention of a proficient to control the quality of processed images. In certain cases, for instance in Ultrasound images, the noise can suppress the information which is valuable for the general practitioner. Consequently medical images can be very inconsistent, and it is crucial to operate case to case. This paper presents a wavelet-based thresholding scheme for noise suppression in Ultrasound images and provides the knowledge about adaptive and anisotropic diffusion techniques for speckle noise removal from different types of images, like Ultrasound.

  5. Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter

    Directory of Open Access Journals (Sweden)

    Hilal Naimi

    2015-01-01

    Full Text Available Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used. The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (StationaryWavelet Transform. We used the SSIM (Structural Similarity Index Measure along with PSNR (Peak Signal to Noise Ratio and SSIM map to assess the quality of denoised images.

  6. Image sequence denoising via sparse and redundant representations.

    Science.gov (United States)

    Protter, Matan; Elad, Michael

    2009-01-01

    In this paper, we consider denoising of image sequences that are corrupted by zero-mean additive white Gaussian noise. Relative to single image denoising techniques, denoising of sequences aims to also utilize the temporal dimension. This assists in getting both faster algorithms and better output quality. This paper focuses on utilizing sparse and redundant representations for image sequence denoising, extending the work reported in. In the single image setting, the K-SVD algorithm is used to train a sparsifying dictionary for the corrupted image. This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations. These modifications lead to substantial benefits in complexity and denoising performance, compared to simply running the single image algorithm sequentially. The algorithm's performance is experimentally compared to several state-of-the-art algorithms, demonstrating comparable or favorable results.

  7. Image denoising via group Sparse representation over learned dictionary

    Science.gov (United States)

    Cheng, Pan; Deng, Chengzhi; Wang, Shengqian; Zhang, Chunfeng

    2013-10-01

    Images are one of vital ways to get information for us. However, in the practical application, images are often subject to a variety of noise, so that solving the problem of image denoising becomes particularly important. The K-SVD algorithm can improve the denoising effect by sparse coding atoms instead of the traditional method of sparse coding dictionary. In order to further improve the effect of denoising, we propose to extended the K-SVD algorithm via group sparse representation. The key point of this method is dividing the sparse coefficients into groups, so that adjusts the correlation among the elements by controlling the size of the groups. This new approach can improve the local constraints between adjacent atoms, thereby it is very important to increase the correlation between the atoms. The experimental results show that our method has a better effect on image recovery, which is efficient to prevent the block effect and can get smoother images.

  8. Research on image denoising based on dictionary training and high-frequency enhancement%基于字典训练和高频增强的图像降噪研究

    Institute of Scientific and Technical Information of China (English)

    宁煌; 曾儿孟; 黄智昌; 靳寒阳

    2016-01-01

    The noise image,especially for the image with high⁃density noise,could lose its many details after denoising. In order to solve this problem,a method based on dictionary learning and high⁃frequency enhancement is proposed in this paper. In this method,the noise image is denoised at first;the adding noise and denoising processes are simulated respectively with the sample image to obtain the denoising sample image , and then the sample image is subtracted from the denoising sample image to get the sample difference image;the sample difference image and the denoising sample image are trained respectively to get a pair of high and low resolution dictionaries,which will be used for rebuilding the high frequency lost when the image is denoised. The simulation results of the experiments show that the proposed method is superior to the BM3D method in the subjec⁃tive human vision and objective evaluation.%噪声图像,特别是含有高密度噪声图像在经过去噪后,图像细节(图像高频)丢失较多。针对这一问题,提出一种基于字典学习和高频增强的方法。该算法首先让噪声图像经过降噪算法处理,然后由样本图像依次模拟加噪和去噪过程得到去噪样本图像,样本图像和去噪样本图像相减得到样本差分图像,最后分别训练样本差分图像和去噪样本图像,得到一对高、低分辨率字典,用于重建图像去噪后所缺失的高频。实验结果表明,所提算法在主观的人眼视觉和客观评价上要优于经典的图像降噪算法。

  9. Fourth-order partial differential equations for effective image denoising

    Directory of Open Access Journals (Sweden)

    Seongjai Kim

    2009-04-01

    Full Text Available This article concerns mathematical image denoising methods incorporating fourth-order partial differential equations (PDEs. We introduce and analyze piecewise planarity conditions (PPCs with which unconstrained fourth-order variational models in continuum converge to a piecewise planar image. It has been observed that fourth-order variational models holding PPCs can restore better images than models without PPCs and second-order models. Numerical schemes are presented in detail and various examples in image denoising are provided to verify the claim.

  10. Medical Image De-Noising Schemes using Wavelet Transform with Fixed form Thresholding

    Directory of Open Access Journals (Sweden)

    Nadir Mustafa

    2015-10-01

    Full Text Available Medical Imaging is currently a hot area of bio-medical engineers, researchers and medical doctors as it is extensively used in diagnosing of human health and by health care institutes. The imaging equipment is the device, which is used for better image processing and highlighting the important features. These images are affected by random noise during acquisition, analyzing and transmission process. This condition results in the blurry image visible in low contrast. The Image De-noising System (IDs is used as a tool for removing image noise and preserving important data. Image de-noising is one of the most interesting research areas among researchers of technology-giants and academic institutions. For Criminal Identification Systems (CIS & Magnetic Resonance Imaging (MRI, IDs is more beneficial in the field of medical imaging. This paper proposes an algorithm for de-noising medical images using different types of wavelet transform, such as Haar, Daubechies, Symlets and Bi-orthogonal. In this paper noise image quality has been evaluated using filter assessment parameters like Peak Signal to Noise Ratio (PSNR, Mean Square Error (MSE and Variance, It has been observed to form the numerical results that, the presentation of proposed algorithm reduced the mean square error and achieved best value of peak signal to noise ratio (PSNR. In this paper, the wavelet based de-noising algorithm has been investigated on medical images along with threshold.

  11. A Novel Approach of Low-Light Image Denoising for Face Recognition

    Directory of Open Access Journals (Sweden)

    Yimei Kang

    2014-04-01

    Full Text Available Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.

  12. Image Restoration and Denoising By Using Nonlocally Centralized Sparse Representation and Histogram Clipping

    Directory of Open Access Journals (Sweden)

    Dr. T. V. S. Prasad Gupta

    2014-10-01

    Full Text Available Due to the degradation of observed image the noisy, blurred, Distorted image can be occurred .for restoring image information we propose the sparse representations by conventional modelsmay not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration,In this method the sparse coding noise is added for image restoration, due to this image restoration the sparse coefficients of original image can be detected. The so-called nonlocally centralized sparse representation (NCSR model is as simple as the standard sparse representation model,for denoising the image here we use the Histogram clipping method by using histogram based sparse representation effectively reduce the noise.and also implement the TMR filter for Quality image.various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed algorithm.

  13. GPU-accelerated denoising of 3D magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Howison, Mark; Wes Bethel, E.

    2014-05-29

    The raw computational power of GPU accelerators enables fast denoising of 3D MR images using bilateral filtering, anisotropic diffusion, and non-local means. In practice, applying these filtering operations requires setting multiple parameters. This study was designed to provide better guidance to practitioners for choosing the most appropriate parameters by answering two questions: what parameters yield the best denoising results in practice? And what tuning is necessary to achieve optimal performance on a modern GPU? To answer the first question, we use two different metrics, mean squared error (MSE) and mean structural similarity (MSSIM), to compare denoising quality against a reference image. Surprisingly, the best improvement in structural similarity with the bilateral filter is achieved with a small stencil size that lies within the range of real-time execution on an NVIDIA Tesla M2050 GPU. Moreover, inappropriate choices for parameters, especially scaling parameters, can yield very poor denoising performance. To answer the second question, we perform an autotuning study to empirically determine optimal memory tiling on the GPU. The variation in these results suggests that such tuning is an essential step in achieving real-time performance. These results have important implications for the real-time application of denoising to MR images in clinical settings that require fast turn-around times.

  14. A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising

    CERN Document Server

    Liyakathunisa,

    2011-01-01

    Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolution images can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. Super-resolution is the process of combining multiple aliased low-quality images to produce a high resolution, high-quality image. The problem of recovering a high resolution image progressively from a sequence of low resolution compressed images is considered. In this paper we propose a novel DCT based progressive image display algorithm by stressing on the encoding and decoding process. At the encoder we consider a set of low resolution images which are corrupted by additive white Gaussian noise and motion blur. The low resolution images are compressed using 8 by 8 blocks DCT and noise is filtered using our proposed novel zonal filter. Multifr...

  15. Advanced numerical methods for image denoising and segmentation

    OpenAIRE

    Liu, Xiaoyang

    2013-01-01

    Image denoising is one of the most major steps in current image processing. It is a pre-processing step which aims to remove certain unknown, random noise from an image and obtain an image free of noise for further image processing, such as image segmentation. Image segmentation, as another branch of image processing, plays a significant role in connecting low-level image processing and high-level image processing. Its goal is to segment an image into different parts and extract meaningful in...

  16. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

    Directory of Open Access Journals (Sweden)

    Chuan He

    2014-01-01

    Full Text Available We propose an adaptive total generalized variation (TGV based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS and the classical augmented Lagrangian method (ALM are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.

  17. 基于小波包分解的整体变分去噪算法%Total variation algorithm of image denoising based on wavelet packet decomposition

    Institute of Scientific and Technical Information of China (English)

    唐昌令; 彭国华

    2013-01-01

    由Rudin等人提出的整体变分(TV)模型被认为是目前最好的图像去噪模型之一.理论表明,TV模型对分块常量的图像去噪效果显著.对于纹理细节丰富的图像,通过引入小波包分解技术,对图像的纹理细节进行多层小波包分解,得到一系列近似分块常量的子图像,用TV模型对子图像分别进行处理,从而图像的纹理细节得到了更好的保留.相对于单独使用TV模型去噪,该方法得到的复原图像峰值信噪比(PSNR)提高了1 dB左右.同时由于采用改进的Bregman迭代方案求解TV模型,算法收敛时间得到了极大的减少.%The Total Variation(TV) model of Rudin et al. for image denoising is considered as one of the best denoising models. Theory suggests that the TV model denoises well piecewise constant images. For the texture rich image, in this paper, it is decomposed by multi-wavelet packet into a series of approximate piecewise constant sub-images. Sub-images are processed separately by TV model. Thus, the texture detail of image is preserved better and the Peak Signal to Noise Ratio (PSNR) of denoised image is improved 1 dB, compared with using TV model alone. At the same time, the improved Bregman iteration scheme is adopted to solve TV model. The algorithm convergence time has been greatly decreased.

  18. Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising

    Directory of Open Access Journals (Sweden)

    Mr. Firas Ali

    2007-01-01

    Full Text Available Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet denoising stage . The choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, arithmetic mean and geometrical mean . The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by this method .Experimental results on test image by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR. Here, to prove the efficiency of this method in image restoration , we have compared this with various restoration methods like Wiener filter alone and inverse filter.

  19. Image denoising method based on dictionary learning with union of two orthonormal bases%基于双正交基字典学习的图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    解凯; 张芬

    2012-01-01

    为了提高图像去除白高斯噪声的性能,利用超完备字典作为图像的稀疏表示.超完备字典的冗余性可以有效地表示图像的各种几何奇异特征.在贝叶斯框架下,以图像块的稀疏表示定义了全局图像先验概率模型,给出了最大后验概率模型下的优化图像去噪算法.超完备字典使用两个不同的正交基构成,给出了基于奇异值分解(SVD)的优化字典计算方法.该方法充分利用正交基的特点,采用SVD方法进行高效的字典学习.基于双正交基字典的去噪算法提高了图像去噪性能,实验结果证实了所提方法的有效性.%Overcomplete dictionary was used to represent an image sparsely in order to improve image denoising performance. The sparse representation may represent efficiently the singular geometry of the images with the redundancy of over-complete dictionary. Global image prior model based on the sparse representation of image patches was presented in Bayesian framework. Then maximum a posteriori probability estimator for denoising image was constructed. The dictionary was composed of two orthonormal bases. A method based on singular value decomposition was used for dictionary learning. The orthonormal property was used to update the one chosen basis effectively. The method can improve the performance of image denoising. The experimental results verify the validity of the method.

  20. A consistent approach for image de-noising using spatial gradient based bilateral filter and smooth filtering

    Science.gov (United States)

    Tiwari, Mayank; Gupta, Bhupendra

    2016-07-01

    We propose an image noise removal method based on spatial gradient based bilateral filter and smooth filtering. Our method consist two step process; in first step, for a given noisy image we extract all of its patches and apply our newly developed spatial gradient based bilateral filter on each patch and get an reference image; in second step we perform smooth filtering on each pixel of the reference image. Experimental results show that our method is consistent and comparable or better than state-of-the-art.

  1. A Novel Super Resolution Reconstruction of Low Reoslution Images Progressively Using DCT and Zonal Filter Based Denoising

    Directory of Open Access Journals (Sweden)

    Liyakathunisa

    2011-02-01

    Full Text Available Due to the factors like processing power limitations and channel capabilities images are often down sampled and transmitted at low bit rates resulting in a low resolution compressed image. High resolutionimages can be reconstructed from several blurred, noisy and down sampled low resolution images using a computational process know as super resolution reconstruction. Super-resolution is the process ofcombining multiple aliased low-quality images to produce a high resolution, high-quality image. The problem of recovering a high resolution image progressively from a sequence of low resolutioncompressed images is considered. In this paper we propose a novel DCT based progressive image display algorithm by stressing on the encoding and decoding process. At the encoder we consider a set of lowresolution images which are corrupted by additive white Gaussian noise and motion blur. The low resolution images are compressed using 8 by 8 blocks DCT and noise is filtered using our proposed novelzonal filter. Multiframe fusion is performed in order to obtain a single noise free image. At the decoder the image is reconstructed progressively by transmitting the coarser image first followed by the detail image. And finally a super resolution image is reconstructed by applying our proposed novel adaptive interpolation technique. We have performed both objective and subjective analysis of the reconstructed image, and the resultant image has better super resolution factor, and a higher ISNR and PSNR. A comparative study done with Iterative Back Projection (IBP and Projection on to Convex Sets (POCS,Papoulis Grechberg, FFT based Super resolution Reconstruction shows that our method has out performed the previous contributions.

  2. Computed tomography perfusion imaging denoising using Gaussian process regression

    Science.gov (United States)

    Zhu, Fan; Carpenter, Trevor; Rodriguez Gonzalez, David; Atkinson, Malcolm; Wardlaw, Joanna

    2012-06-01

    Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.

  3. 基于空间分数阶偏微分方程的图像去噪模型研究%Research on Image Denoising Based on Space Fractional Partial Differential Equations

    Institute of Scientific and Technical Information of China (English)

    黄果; 许黎; 陈庆利; 蒲亦非

    2012-01-01

    为了在获取更高信噪比的同时更多地保留图像边缘和纹理等细节信息,将分数阶微积分理论和偏微分方程方法有效结合,构建了基于空间分数阶偏微分方程的图像去噪模型,并利用分数阶微分掩模算子来实现去噪模型的数值计算。该去噪模型通过引入以分数阶梯度模值为参数的边缘停止函数并选择合适的分数阶微分阶次,由此能够在一定程度上解决传统去噪模型存在的不足之处。实验结果表明,基于空间分数阶偏微分方程的图像去噪模型较传统的去噪模型不仅可以提高图像的信噪比,而且可以更好地保留图像边缘和纹理等细节信息。%In order to preserve more edge and texture information of image while obtaining higher value of signal-to-noise,the image denoising model based on space fractional partial differential equations was constructed by the effective combination of fractional calculus theory and partial differential equations method,and the numerical of denoising model was achieved using fractional differential mask operator.This denoising model could solve existing problems of the traditional denoising model to a certain extent by introducing the edge stopping function to the parameters of fractional grads modulus and selecting the appropriate order of fractional differential.The experimental results showed that compared with the traditional image denoising models,the image denoising model based on space fractional partial differential equations not only enhanced the signal-to-noise ratio of image but also better retained the edge and texture details information of image.

  4. Several Kinds of Image De-noising Methods Based on Matlab%基于Matlab的几种图像去噪方法研究

    Institute of Scientific and Technical Information of China (English)

    赛地瓦尔地·买买提

    2013-01-01

    In order to research on the quality of denoising algorithms,the paper introduces the principle and methods of eliminating image noise,using the tradition methods such as linear,nonlinear and frequency domain to eliminate image noise,and their results of eliminating image noise are compared. Finally,better denoising effect of averaging filtering on Gaussian noise and good denoising effect of median filtering on salt and pepper noise are demonstrated by simulation using Matlab. Through Wiener filtering,the Gaussian noise is inhibited obviously,and denoising effect of the wavelet transform method on the low amplitude noise and unwanted image noise is satisfactory.%为了研究几种图像去噪方法的优劣,在介绍图像去噪的基本方法与原理的基础上,应用传统的线性、非线性以及频域的方法对含高斯噪声,椒盐噪声的图像进行去噪,然后对去噪效果进行分析比较和仿真实现。最后,通过Matlab进行仿真验证了邻域平均法对高斯噪声抑制是比较好的,维纳滤波对高斯噪声有明显的抑制作用,中值滤波对椒盐噪声的去噪效果很好,小波变换法对低幅值的噪声和不期望的图像噪声的去噪效果令人满意。

  5. A new study on mammographic image denoising using multiresolution techniques

    Science.gov (United States)

    Dong, Min; Guo, Ya-Nan; Ma, Yi-De; Ma, Yu-run; Lu, Xiang-yu; Wang, Ke-ju

    2015-12-01

    Mammography is the most simple and effective technology for early detection of breast cancer. However, the lesion areas of breast are difficult to detect which due to mammograms are mixed with noise. This work focuses on discussing various multiresolution denoising techniques which include the classical methods based on wavelet and contourlet; moreover the emerging multiresolution methods are also researched. In this work, a new denoising method based on dual tree contourlet transform (DCT) is proposed, the DCT possess the advantage of approximate shift invariant, directionality and anisotropy. The proposed denoising method is implemented on the mammogram, the experimental results show that the emerging multiresolution method succeeded in maintaining the edges and texture details; and it can obtain better performance than the other methods both on visual effects and in terms of the Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) values.

  6. An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm

    OpenAIRE

    Marc Lebrun; Arthur Leclaire

    2012-01-01

    K-SVD is a signal representation method which, from a set of signals, can derive a dictionary able to approximate each signal with a sparse combination of the atoms. This paper focuses on the K-SVD-based image denoising algorithm. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.

  7. 基于曲波变换和小波变换的图像去噪算法%Image Denoising Method Based on Curvelet and Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    王海松; 王伟

    2009-01-01

    针对应用曲波变换进行图像处理过程中所产生的伪影现象,提出一种基于快速离散曲波变换和小波变换的联合去噪算法.对噪声图片分别采用非抽样小波变换和快速离散曲波变换进行去噪,并对经过快速离散曲波变换去噪后的图像进行四叉树分解,根据分解结果对图像进行首构得到最终融合图像.实验结果表明,联合去噪算法对图像的边缘和均匀区域都有较好的去噪效果,能够有效抑制伪影,具有较高的峰值信噪比.%According to the artifacts that appear in the result images after applying curvelet denoising approach, a new method for denoising images corrupted with additive white Gaussian noise is proposed. Using Undecimated Wavelet Transform(UDWT) and Fast Discrete Curvelet Transform(FDCT) respectively for coding noisy image, applying quadtree decomposition to the result of FDCT approach and combining the attributes of both transforms by analyzing the result of quadtree decomposition, it is possible to get better effect. Experimental result shows that the Combined Denoising Method(CDM) denoises effectively in both homogeneous areas and areas with edges, suppresses artifacts evidently and has high peak signal-to-noise ratio.

  8. Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

    Institute of Scientific and Technical Information of China (English)

    Yanqin Zhao

    2013-01-01

    Based on low illumination and a large number of mixed noises contained in coal mine,denoising with one method usually cannot achieve good results,So a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented.Firstly,we used directional median filter to effectively reduce impulse noise in the spatial domain,which is the main cause of noise in mine.Secondly,we used a Wiener filtration method to mainly reduce the Gaussian noise,and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain.This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits,and effectively reduce impulse noise and Gaussian noise in a coal mine,while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

  9. Curvelet Transform-Based Denoising Method for Doppler Frequency Extraction

    Institute of Scientific and Technical Information of China (English)

    HOU Shu-juan; WU Si-liang

    2007-01-01

    A novel image denoising method based on curvelet transform is proposed in order to improve the performance of Doppler frequency extraction in low signal-noise-ratio (SNR) environment. The echo can be represented as a gray image with spectral intensity as its gray values by time-frequency transform. And the curvelet coefficients of the image are computed. Then an adaptive soft-threshold scheme based on dual-median operation is implemented in curvelet domain. After that, the image is reconstructed by inverse curvelet transform and the Doppler curve is extracted by a curve detection scheme. Experimental results show the proposed method can improve the detection of Doppler frequency in low SNR environment.

  10. 基于边缘值、方差以及脉冲性的偏微分方程图像去噪算法%Partial differential equation image denoising algorithm based on Edge value, variance and impulsiveness

    Institute of Scientific and Technical Information of China (English)

    侯传勇

    2013-01-01

      近二十年来,随着计算机技术的发展,图像处理得到了越来越多的关注和研究。图像去噪是图像处理中的重要环节,对于改善图像质量具有重要的意义。近年来的研究表明,基于偏微分方程的图像去噪已经取得了显著的成效。本文提出了一种改进的基于偏微分方程的去噪算法。图像的边缘值、方差、脉冲性可以表示图像的区域分布变化,本文将以上三种图像的特征信息引入偏微分模型中,使得在去噪的同时可以较好的保持图像的边缘细节信息。实验结果表明,改进的偏微分模型具有较好的去噪效果。%  In the past two decades,with the development of computer technology,image processing has given more and more attention and study. The Image denoising is an important part of the image processing,has important implications for improving image quality. Recent studies shown that the partial differential equations-based image denoising has achieved significant results. In this paper,on the basis of the original partial differential equations,an improved denoising method based on partial differential equations is proposed. The method uses edge value,variance,image impulse to represent the distribution changes in the characteristics of the image region,therefore,this method can better maintain the edge details of the image during the same time as denoising. Experimental results show that the denoising effect by the improved partial differential model is well.

  11. Partial differential equation method based on image feature for Denoising%基于图像特征的偏微分方程去噪方法

    Institute of Scientific and Technical Information of China (English)

    张小华; 王然

    2011-01-01

    针对不同的自然图像去噪,现有方法的处理结果往往都含有吉布斯效应,目前很难找到非常理想的方法来进行处理。文中提出了一种基于图像特征的偏微分方程图像去噪方法。文中在研究TV模型和PM模型的基础上提出了基于每幅图像具体特征的去噪模型。该模型能够自适应的根据图像每个区域内的细节特征来调节扩散系数的大小,使其能在消除高梯度噪声的同时较好的保留边缘信息。我们证明了该模型的理论性。实验表明改进后的方法在消除噪声的同时也消除了吉布斯现象。%For the special different nature images, we could hardly find particularly desirable approach, and there always exist Gibbs-type artifacts in the results of most methods. A novel Partial Differential Equation (PDE) model is proposed based on image feature for images denoising. The PDE model is adaptive within each region according to the details of the image feature to adjust the size of the diffusion coefficient. So it can be disposed the high gradient noise at the same time better to retain the edge information. We also analyze the performance of the PDE model method. Numerical results show that our algorithm competes favorably with state of the-art TV projection methods to eliminate noise and reduce Gibbs-type artifacts.

  12. 一种改进的基于K-SVD字典的图像去噪算法%An improved image denoising algorithm based on K-SVD dictionary

    Institute of Scientific and Technical Information of China (English)

    王欣; 沈思秋

    2014-01-01

    In order to achieve the effects of image denoising better, the improved image denoising algorithm based on K-SVD dictionary is designed in this paper. First, the input noisy signal is decomposew3d by using K-means clustering. With the decomposing image blocks, the signal will have sparse Bayesian learning and noise updating. When iterating the given numbers, the signal continues to use OMP algorithm in order to realize the sparse coding. With the completion of sparse coding, the method will update the dictionary by columns using singular value decomposition, iteratively to achieve the completely dictionary and finish sparse representation of image. Eventually, the method will restore original image and obtain the denoising image. Different kinds of images with different noise levels are used to test the algorithm. The experiments and results show that, comparing to the traditional K-SVD dictionary image denoising , our method has better ability of denoising under the premise of keeping the information of image edge and detail and has better vision effection.%为了更好地实现图像的去噪效果,提出了一种改进的基于K-SVD(Singular Value Decomposition)字典学习的图像去噪算法。首先,将输入的含噪信号进行K均值聚类分解,将得到的图像块进行稀疏贝叶斯学习和噪声的更新,当迭代到一定次数时继续使用正交匹配追踪(Orthogonal Matching Pursuit, OMP)算法对图像块进行稀疏编码,然后在完成稀疏编码的基础上通过奇异值分解来逐列更新字典,反复迭代至得到过完备字典以实现稀疏表示,最后对处理过的图像进行重构,得到去噪后的图像。实验结果表明,本文的改进算法相对于传统的K-SVD字典的图像去噪能够在保留图像边缘和细节信息的同时,更有效地去除图像中的噪声,具有更好的视觉效果。

  13. 基于拉普拉斯算子和图像修补的图像去噪算法%Image denoising algorithms based on Laplacian operator and image inpainting

    Institute of Scientific and Technical Information of China (English)

    田素云; 王小明; 赵雪青

    2012-01-01

    通过分析偏微分方程(PDE),设计了基于拉普拉斯算子和图像修补的图像去噪算法用于处理被噪声污染的图像:ROF调和拉普拉斯(RHL)算法和ROF调和修补(RHI)算法.通过分析图像的局部特征,结合ROF模型在处理图像时具有边缘保护能力,调和模型在处理图像平滑区域时能够避免产生“阶梯效应”和拉普拉斯算子具有增强细节信息的特点,设计了RHL算法;在RHL算法的基础上,结合基于PDE的图像修补模型设计了RHI算法.实验结果表明,设计的RHL算法和RHI算法既克服了ROF模型、调和模型在去除图像噪声时的缺点,又结合了两者的优点,与其他基于PDE的算法相比,在去除图像噪声、处理图像平滑区域、保持图像边缘细节信息方面都有较好的性能.%Through the analysis of Partial Differential Equation (PDE), the image denoising algorithms based on Laplacian operator and image inpainting were designed for the processing of the polluted image by noise: Rudin-Osher-Fatemi (ROF) harmonical Laplacian algorithm and ROF harmonical inpainting algorithm, which were simply called RHL and RHI respectively. By analyzing the local features of the image, the ability of the ROF model in protecting image edges and the harmonical model in overcoming the "ladder effect", and the advantages of the Laplacian operator in enhancing edges, the first image denoising algorithm, RHL was designed. Meanwhile, the second algorithm RHI was designed by syncretizing the image inpainting model. The experimental results show that the two designed algorithms, RHL and RHI, have better performance visually and quantitatively than other algorithms, which combine the advantages of the ROF model and harmonical model in image denoising effectively. Compared with other PDE based algorithms, the two designed algorithms can remove noise, protect smooth region and edge information much better.

  14. Two-direction nonlocal model for image denoising.

    Science.gov (United States)

    Zhang, Xuande; Feng, Xiangchu; Wang, Weiwei

    2013-01-01

    Similarities inherent in natural images have been widely exploited for image denoising and other applications. In fact, if a cluster of similar image patches is rearranged into a matrix, similarities exist both between columns and rows. Using the similarities, we present a two-directional nonlocal (TDNL) variational model for image denoising. The solution of our model consists of three components: one component is a scaled version of the original observed image and the other two components are obtained by utilizing the similarities. Specifically, by using the similarity between columns, we get a nonlocal-means-like estimation of the patch with consideration to all similar patches, while the weights are not the pairwise similarities but a set of clusterwise coefficients. Moreover, by using the similarity between rows, we also get nonlocal-autoregression-like estimations for the center pixels of the similar patches. The TDNL model leads to an alternative minimization algorithm. Experiments indicate that the model can perform on par with or better than the state-of-the-art denoising methods.

  15. Image denoising based on Poisson-like noise model%基于泊松噪音模型的图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    赵梦柳; 李宏伟

    2012-01-01

    In computed tomography,the projection data are usually thought of being corrupted by Poisson noise.However,there is no specific noise model for CT images constructed from sino-data.Nevertheless,the gray values of CT images bare some characteristics of Poisson distribution.So we can regard that CT images are corrupted by Poisson-like noise.In this paper,we propose a denoising model for Poisson-like noise,whose effectiveness has been validated by numerical experiments implemented on synthesized as well as real CT data.Furthermore,our model could be generalized to deal with mixture model noiseor even noise with unknown type.A fast numerical algorithm is also developed based on a dual formulation and an iterative relaxation technique.%通常认为,CT中的投影数据带有泊松噪音.然而目前尚未有定论,从投影数据重建得到的CT图像带有何种类型的噪音.由于CT图像的灰度值呈现一定泊松分布的性质,因此我们可以假定CT图像被“类泊松”噪音所污染.本文中,我们提出一种去除“类泊松”噪音的模型,基于仿真数据和真实CT数据的数值实验结果证明了该模型的有效性.此外,该模型可以被扩展到混合噪音模型甚至未知类型的噪音上.我们还提出了一种基于对偶和松弛迭代技术的快速数值算法.

  16. 图像去噪的混合滤波方法%Method of Video Image De-Noising Based on Mixed Filter

    Institute of Scientific and Technical Information of China (English)

    项力领; 刘智; 齐冀; 杨阳

    2013-01-01

    Video image mixed noise by Gaussian noise and impulse noise,seriously affected the image storage,coding and decoding,transmission,target identification and tracking post-processing.A mixed filtering method has been proposed based on an average of edge detection.Through the number of impulse noise judgment methods,the impulse noise is separated from the mixed noise and removed using median filtering.The edge of new image will be extracted by an average of edge detection method and then the Gaussian noise of non-edge is filtered using adaptive mean filtering methods.The edge of the new image is embedded in the filtered Gaussian noise images.Experiment results show that the method will be able to effectively remove Gaussian noise and impulse noise of the image,and to maintain the edges of the image information,improving the image de-noising and clarity.%针对视频图像在同时受到高斯噪声和脉冲噪声污染时,严重影响图像的存储、编解码、传输、目标识别与跟踪的问题,提出一种图像去噪的混合滤波方法.该方法通过基于个数判断脉冲噪声的方法,将脉冲噪声从混合噪声中分离,并利用中值滤波将其过滤;再利用分块平均边缘检测的方法提取图像的边缘;利用自适应均值滤波方法滤除非边缘的高斯噪声,并将边缘图像嵌入滤除高斯噪声的图像中.实验结果表明,该方法不但能有效去除图像中的高斯噪声和脉冲噪声,而且能保持图像的边缘信息,从而提高图像的去噪效果和清晰度.

  17. Denoising Algorithm Based on Generalized Fractional Integral Operator with Two Parameters

    Directory of Open Access Journals (Sweden)

    Hamid A. Jalab

    2012-01-01

    Full Text Available In this paper, a novel digital image denoising algorithm called generalized fractional integral filter is introduced based on the generalized Srivastava-Owa fractional integral operator. The structures of n×n fractional masks of this algorithm are constructed. The denoising performance is measured by employing experiments according to visual perception and PSNR values. The results demonstrate that apart from enhancing the quality of filtered image, the proposed algorithm also reserves the textures and edges present in the image. Experiments also prove that the improvements achieved are competent with the Gaussian smoothing filter.

  18. Image denoising model based on variational partial differential equation with double parameters%基于变分偏微分方程的双参数图像去噪模型

    Institute of Scientific and Technical Information of China (English)

    胡晓红; 陈大卿

    2012-01-01

    给出了一种基于变分偏微分方程的双参数图像去噪模型.利用变分法的极大极小原理,证明了该双参数模型存在唯一的极小值,给出该模型的Euler-Lagrange方程,根据给出的偏微分方程的离散格式,对噪声图像进行去噪,与Rudin,Osher and Fatemi提出的模型(ROF模型)的结果比较,结果表明,双参数模型在视觉上及峰度信噪比上都比ROF模型的去噪效果好.%This paper presents an image denoising model based on Variational Partial differential equation with double parameters. Firstly,using the minimax principle of variational method,this paper proves that the double parameters model has an unique minimal value,then it gives the corresponding Euler-Lagrange equation. Secondly,According to the given discrete format of partial differential equation,it denoises some noise images. Comparing the results with Rudin,Osher and Fatemi model(ROF model) ,the results show that double parameters model in visual and kurtosis signal-to-noise ratio has better denoising effect.

  19. An approach for SLAR images denoising based on removing regions with low visual quality for oil spill detection

    Science.gov (United States)

    Alacid, Beatriz; Gil, Pablo

    2016-10-01

    This paper presents an approach to remove SLAR (Side-Looking Airborne Radar) image regions with low visual quality to be used for an automatic detection of oil slicks on a board system. This approach is focused on the detection and labelling of SLAR image regions caused by a poor acquisition from two antennas located on both sides of an aircraft. Thereby, the method distinguishes ineligible regions which are not suitable to be used on the steps of an automatic detection process of oil slicks because they have a high probability of causing false positive results in the detection process. To do this, the method uses a hybrid approach based on edge-based segmentation aided by Gabor filters for texture detection combined with a search algorithm of significant grey-level changes for fitting the boundary lines in each of all the bad regions. Afterwards, a statistical analysis is done to label the set of pixels which should be used for recognition of oil slicks. The results show a successful detection of the ineligible regions and consequently how the image is partitioned in sub-regions of interest in terms of detecting the oil slicks, improving the accuracy and reliability of the oil slick detection.

  20. Image denoising via clustering-based sparse representation over collaborative filter%基于联合滤波的聚类稀疏表示图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    高美凤; 王晨

    2015-01-01

    针对非局部均值去噪算法中噪声对结构聚类影响的问题,提出了一种基于联合滤波预处理的聚类稀疏表示图像去噪算法.利用维纳滤波和巴特沃斯滤波联合滤波处理提取含噪图像中的高频分量,同时减小了噪声对聚类的影响;利用非局部均值去噪的思想将高频图像块进行聚类,每一类图像块单独进行字典学习,增强字典的自适应性;利用多循环字典更新的K-SVD算法进行类内字典学习,增强字典的描述能力.实验结果表明,与传统的K-SVD算法相比,该算法能有效保留图像的结构信息,并且提升了图像的去噪效果.%For the influence of noise for clustering in non-local means denoising algorithm, a denoising algorithm based on collaborative filter and clustering-based sparse representation is presented. It employs Wiener filter and Butterworth filter to extract high-frequency components on the noisy image, and simultaneously reduces the influence of noise for clustering. The high-frenquency image blocks that are extracted from the noisy image are clustered by using the non-local means denoising. The adaptive ability of dictionary is enhanced because each block runs dictionary learning independently. Then structured dictionaries are learned by using several dictionary update cycles-based K-SVD instead of K-SVD. It rein-forces the descriptive ability of dictionary. The experiments show that the modified algorithm, which is compared with the traditional K-SVD denoising algorithm, can protect the information of image structure effectively and promote the result of denoising greatly.

  1. Denoising Method of Image Based on Undecimated Curvelet Transform%基于非抽取Curvelet变换的图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    罗鹏

    2011-01-01

    原始的Curvelet变换在Radon域用正交小波变换得到Curvelet系数,然而正交小波不具有平移不变性,所以会产生Gibbs震荡现象.提出用非抽取小波变换代替原始的Curvelet变换中的正交小波变换.非抽取小波的平移不变性和Curvelet变换的高度方向敏感性使得新算法成为图像去噪的一个很好的选择.使用数字非抽取Curvelet变换对添加了高斯白噪声的标准图像进行去噪.实验结果表明,新算法无论从峰值信噪比还是从视觉效果上都要优于小波去噪,普通Curvelet图像去噪和Wiener2滤波.而且新方法在去除噪声的同时还较好地保留了图像的边缘信息.%A novel image denoising method was proposed by incorporating the undecimated Curvelet wavelets into the ordinary curvelet transform. Curvelet coefficients were acquired by using orthogonal wavelet transform in radon domain. Traditional discrete wavelet transform is not shiftable, so it will lead to Gibbs phenomenon. The orthogonal wavelet was replaced with undecimated wavelet transform in the last step of the Curvelet transform. The shift invariant property of the undecimated wavelet and the high directional sensitivity of the curvelet transform made the new method a good choice for image denoising. The digital undecimated Curvelet transform was applied to denoise some standard images corrupted with additive Gaussian white noise. Experimental results showed that the proposed method performanced better than the ordinary curvelet image denoising, and wiener2 filter in terms of both peak signal-to-noise ratio and visual quality. In particular, the method preserved the shape edges better while removing white noise.

  2. A Robust and Fast Non-Local Means Algorithm for Image Denoising

    Institute of Scientific and Technical Information of China (English)

    Yan-Li Liu; Jin Wang; Xi Chen; Yan-Wen Guo; Qun-Sheng Peng

    2008-01-01

    In the paper, we propose a robust and fast image denoising method. The approach integrates both Non- Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm - similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.

  3. Denoising portal images by means of wavelet techniques

    Science.gov (United States)

    Gonzalez Lopez, Antonio Francisco

    Portal images are used in radiotherapy for the verification of patient positioning. The distinguishing feature of this image type lies in its formation process: the same beam used for patient treatment is used for image formation. The high energy of the photons used in radiotherapy strongly limits the quality of portal images: Low contrast between tissues, low spatial resolution and low signal to noise ratio. This Thesis studies the enhancement of these images, in particular denoising of portal images. The statistical properties of portal images and noise are studied: power spectra, statistical dependencies between image and noise and marginal, joint and conditional distributions in the wavelet domain. Later, various denoising methods are applied to noisy portal images. Methods operating in the wavelet domain are the basis of this Thesis. In addition, the Wiener filter and the non local means filter (NLM), operating in the image domain, are used as a reference. Other topics studied in this Thesis are spatial resolution, wavelet processing and image processing in dosimetry in radiotherapy. In this regard, the spatial resolution of portal imaging systems is studied; a new method for determining the spatial resolution of the imaging equipments in digital radiology is presented; the calculation of the power spectrum in the wavelet domain is studied; reducing uncertainty in film dosimetry is investigated; a method for the dosimetry of small radiation fields with radiochromic film is presented; the optimal signal resolution is determined, as a function of the noise level and the quantization step, in the digitization process of films and the useful optical density range is set, as a function of the required uncertainty level, for a densitometric system. Marginal distributions of portal images are similar to those of natural images. This also applies to the statistical relationships between wavelet coefficients, intra-band and inter-band. These facts result in a better

  4. Improved Image Denoising Method Based on High Density Discrete Wavelet Transform%基于高密度离散小波变换的改进图像降噪方法

    Institute of Scientific and Technical Information of China (English)

    李昌顺; 杨浩; 裴蕾

    2012-01-01

    To improve the quality of the image, this paper presents an improved image denoising method based on high density discrete wavelet transform. The two-dimensional fast decomposition and reconstruction algorithm is given, and it is used to decompose the image in multi-scale. The wavelet coefficients at each level are processed with bivariate shrinkage threshold according to the correlation of wavelet coefficients of adjacent scales. The denosed image is reconstructed. Experiments show that compared with other wavelet denoising method, the method proposed in the paper further enhances the image denoising performance, and still keeps the details of the image.%为进一步提高图像质量,提出一种基于高密度离散小波变换的改进图像降噪方法.给出二维高密度离散小波变换的分解与重构快速算法,通过该算法对图像进行多尺度分解,利用相邻尺度小波系数相关性对各层小波系数进行双变量收缩阈值处理,重构降噪后的图像.实验结果表明,与其他常用小波降噪方法相比,该方法能进一步提高图像降噪效果,且在降噪过程中较好地保留图像细节.

  5. Image Denoising Based on Wavelet Transform and Median Filter%一种基于中值滤波和小波变换的图像去噪算法研究

    Institute of Scientific and Technical Information of China (English)

    万小红

    2012-01-01

    针对同时含有脉冲噪声和高斯噪声的混合含噪图像特点,结合自适应中值滤波和小波变换的阈值滤波的各自优点,提出了一种基于中值滤波和小波变换阈值去噪相结合的图像去噪方法,即先对图像进行自适应中值滤波去除脉冲噪声,然后利用小波变换去除剩余的高斯噪声.实验表明:该方法能在有效去除混合噪声的同时,较好地保持边缘和细节信息.%As for the features of the noise image mixed with the impulse noise and the Gaussian noise, combined with the respective merits of the adaptive median filter and wavelet transform threshold denoising, a kind of denoising method based on the combination of the median filter and wavelet threshold denoising is put forward. Firstly,remove the impulsive noise in the adaptive median filter, and then removes the Gaussian noise by using wavelet transform. Experiments show that this method can effectively remove the mixture noise. At the same time, it also can maintain the edge details of the information.

  6. [A fast non-local means algorithm for denoising of computed tomography images].

    Science.gov (United States)

    Kang, Changqing; Cao, Wenping; Fang, Lei; Hua, Li; Cheng, Hong

    2012-11-01

    A fast non-local means image denoising algorithm is presented based on the single motif of existing computed tomography images in medical archiving systems. The algorithm is carried out in two steps of prepossessing and actual possessing. The sample neighborhood database is created via the data structure of locality sensitive hashing in the prepossessing stage. The CT image noise is removed by non-local means algorithm based on the sample neighborhoods accessed fast by locality sensitive hashing. The experimental results showed that the proposed algorithm could greatly reduce the execution time, as compared to NLM, and effectively preserved the image edges and details.

  7. Generalized variation-based regularization method for infrared image denoising%基于广义变分正则化的红外图像噪声抑制方法

    Institute of Scientific and Technical Information of China (English)

    钱伟新; 王婉丽; 祁双喜; 程晋明; 刘冬兵

    2014-01-01

    文中提出了一种广义变分正则化的红外图像噪声抑制方法,该方法采用p-范数代替目前广泛被采用的全变分范数作为正则项,构造了用于抑制图像噪声的展平泛函,从而将图像噪声抑制问题转化为能量泛函优化问题。通过推导,得到了相应的用于图像噪声抑制的非线性偏微分方程,并采用固定点迭代算法进行线性化求解,使得迭代解稳定收敛。数值试验结果表明,该方法能够有效地去除图像噪声,较之全变分图像噪声抑制方法,新方法进一步提高了对小宽度图像边缘的保持能力,是一种有效且性能优良的红外图像噪声抑制方法。%A generalized variation (GV) regularization based infrared image denoising method was proposed in this paper. In the new method, a p-norm was used as regularized term to replace total variation (TV) norm in traditional TV based image denoising methods which were used popular in image processing domain. Then a smoothing functional was constructed for noised removal. Thus, the problem of image denoising was transformed to a problem of a functional minimization. A nonlinear partial differential equation (PDE) was deduced from the new image denoising model. To solve the nonlinear PDE, the fixed point iteration (FPI) scheme was introduced to linear the PDE. The stability and convergence of regularized solution were ensured by FPI scheme. The numerical experimental results show that comparison with TV regularized method, the GV regularized method can preserve image edge including those small width edges more efficiently while removing noise. The GV regularized method is an efficient image noise removed method with better performance of noise removal and edge preserving.

  8. A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

    Science.gov (United States)

    Huang, Jun; Ma, Yong; Mei, Xiaoguang; Fan, Fan

    2016-11-01

    The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein's unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.

  9. Hand Depth Image Denoising and Superresolution via Noise-Aware Dictionaries

    Directory of Open Access Journals (Sweden)

    Huayang Li

    2016-01-01

    Full Text Available This paper proposes a two-stage method for hand depth image denoising and superresolution, using bilateral filters and learned dictionaries via noise-aware orthogonal matching pursuit (NAOMP based K-SVD. The bilateral filtering phase recovers singular points and removes artifacts on silhouettes by averaging depth data using neighborhood pixels on which both depth difference and RGB similarity restrictions are imposed. The dictionary learning phase uses NAOMP for training dictionaries which separates faithful depth from noisy data. Compared with traditional OMP, NAOMP adds a residual reduction step which effectively weakens the noise term within the residual during the residual decomposition in terms of atoms. Experimental results demonstrate that the bilateral phase and the NAOMP-based learning dictionaries phase corporately denoise both virtual and real depth images effectively.

  10. Image Denoising Algorithm Based on Elliptic Directional Windows in Wavelet Domain%椭圆方向窗内的小波域图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    金彩虹

    2015-01-01

    Exploiting the different spread characteristics of noise and information coefficients in the quadtree struc-ture, an image denoising algorithm based on elliptic directional windows in wavelet domain is proposed,in which the quadtree structure is first used to divide the wavelet coefficients into“noise” coefficients,“image” coefficients and “ mixture” coefficients. Then those “noise” coefficients are set to zero, and those “image” coefficients are kept completely. Finally utilizing the advantages of multidirection-selectivity, an optimal elliptic directional window is obtained, using the stein unbiased risk estimation in the wavelet domain, and those “mixture” coefficients are estimated in this optimal elliptic directional windows by minimum mean squared error criterion. The experimental results show that this method has effectively separated noise and image details, improved the accuracy of variance estimation, kept more image details and improved the peak signal-to-noise ratio.%利用噪声小波系数父子和图像小波系数父子在四叉树上的不同传播特性,首先,将小波系数区分为:噪声系数、图像系数和噪声与图像共存的系数(简称共存系数)。然后,将噪声系数置零,图像系数完整保留。最后,利用小波域内子带能量分布的方向聚类性,采用Stein无偏风险估计,为每个子带确定最佳大小的椭圆方向邻域窗,通过最小均方误差准则在该窗内对共存系数进行去噪估计。实验结果表明,该算法实现了信号和噪声的有效分离,提高了真实信号系数方差估计的准确度,在去除噪声的同时尽可能多地保留了图像的边缘细节,提高了恢复图像的PSNR值。

  11. Image denoising in bidimensional empirical mode decomposition domain: the role of Student's probability distribution function.

    Science.gov (United States)

    Lahmiri, Salim

    2016-03-01

    Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT). Two biomedical images and a standard digital image were considered for experiments. The original images were corrupted with additive Gaussian noise with three different levels. Based on peak-signal-to-noise ratio, the experimental results show that PDE, LCDP, NLCDP, and DWT all perform better in the tBEMD than in the classical BEMD domain. It is also found that tBEMD is faster than classical BEMD when the noise level is low. When it is high, the computational cost in terms of processing time is similar. The effectiveness of the presented approach makes it promising for clinical applications.

  12. Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect

    Directory of Open Access Journals (Sweden)

    Feng-Ping An

    2015-01-01

    Full Text Available Bidimensional empirical mode decomposition (BEMD algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model constructed by the support vector machine (SVM method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio.

  13. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  14. An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm

    Directory of Open Access Journals (Sweden)

    Marc Lebrun

    2012-05-01

    Full Text Available K-SVD is a signal representation method which, from a set of signals, can derive a dictionary able to approximate each signal with a sparse combination of the atoms. This paper focuses on the K-SVD-based image denoising algorithm. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.

  15. Infrared imaging denoising processing based on regular granule resampling algorithm%基于正则粒子重采样算法的红外成像消噪处理

    Institute of Scientific and Technical Information of China (English)

    陈淑静; 马天才

    2009-01-01

    针对红外成像消噪的粒子滤波退化问题,提出正则粒子重采样算法.该算法从粒子群重采样获得粒子云(x_k~j,n_j)_j~m=1,解决了粒子多样性消失的问题并克服粒子匮乏的现象;接着又通过添加辅助粒子v,将下一时刻观测值权值大的粒子进行标识,使粒子权值ω_k~i∝(p (x_k|y_(k-1)~i))/(p (x_k|μ_(k-1)~i))更加稳定;给出了运动物体的红外成像消噪模型.实验仿真表明:正则粒子重采样算法通过添加辅助粒子使红外成像消噪效果好,成像清晰度在95%以上.%The regular granule heavy sampling algorithm is proposed for solving the deterioration of the granules in the infrared imaging denoising process. The granule cloud is obtained by the algorithm based on the granule resampling which can eliminate the phenomena of the granule diversity vanishing and granule want, the granules with great weight value observed at the next moment are marked to make the granule weight value more stable by adding some auxiliary granules, and then an infrared imaging denoising model of a moving object is established. The experimental result indicates that the method makes the effect of the infrared imaging denoising much better, and the imaging definition above 95%.

  16. Image Denoising Based on Wavelet Transform Direction and SVD%基于小波变换方向信息的奇异值图像去噪研究

    Institute of Scientific and Technical Information of China (English)

    王敏; 周树道; 叶松

    2012-01-01

    提出一种基于小波变换方向信息的奇异值图像分解去噪方法.由于图像噪声主要集中在小波域中的高频子图部分,且系数较小,可以利用奇异值分解后较大的奇异值和对应的特征向量重构出去噪图像,然而由于奇异值分解固有的行列方向性,对于高频对角线子图重构出的图像去噪效果不理想,故采取旋转至行列方向后再进行常用的奇异值滤波.低频子图仅作简单维纳滤波,最后将去噪后的低频和高频子图进行小波反变换重构出最终的去噪图像.实验结果表明,该方法在有效去噪的同时较好地保留了原有的高频细节信息.%An optimized image denoising algorithm is proposed based on wavelet transform directional information and SVD. As the image noise is mainly concentrated in the high-frequency, and the coefficient is small, using singular value decomposition of large singular values and corresponding eigenvectors reconstructed image noise out, but because of the inherent direction of the singular value decomposition, denoising result of diagonal sub-image reconstructed is not satisfactory, we rotate the diagonal sub-image to the level (vertical) ,then use the singular value filtering, at low-frequency only use simple wiener filter, and finally use anti-wavelet transform to reconstruct the denoising image. Experimental results show that this method is effective in denoising, while it retains the original details.

  17. Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

    Science.gov (United States)

    Pnevmatikakis, Eftychios A; Soudry, Daniel; Gao, Yuanjun; Machado, Timothy A; Merel, Josh; Pfau, David; Reardon, Thomas; Mu, Yu; Lacefield, Clay; Yang, Weijian; Ahrens, Misha; Bruno, Randy; Jessell, Thomas M; Peterka, Darcy S; Yuste, Rafael; Paninski, Liam

    2016-01-20

    We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

  18. An Improved Image Denoising Algorithm Based on Partial Differential Equation%一种改进的基于偏微分方程图像的降噪算法

    Institute of Scientific and Technical Information of China (English)

    董婵婵; 武怀彬; 张芳; 高小帆; 桂志国

    2014-01-01

    Concerning the contradiction between edge -preserving and noise -suppressing in the process of image denoising ,an improved image denoising method was proposed based on partial differential equation (PDE) model .Regularization and rank-ordered absolute differences were introduced into the proposed algo-rithm ,which constructed a new diffusion coefficient combined with the model of Chao and Tsai .The method takes advantages of the regulation and rank-ordered absolute differences ,so it can solve ill-posed problem of the equation and distinguish between noise and edges more effectively .Experimental results show that the proposed method can retain more information of details and get rid of noise better than the other common PDE models ,and improve the SNR of image denoising .%针对图像去噪过程中存在边缘保持与噪声抑制之间的矛盾,提出了一种改进的基于偏微分模型的图像去噪算法。引入了正则化、绝对差值排序检测法,结合C hao和 T sai模型,构造了一种新的扩散系数函数,兼具了正则化解决方程病态问题和绝对差值排序检测法有效区分噪声与边缘的优点。实验结果表明:与其他基于常用偏微分模型的去噪算法相比,所提算法能更加有效地去除噪声,保留了更多的细节信息,提高了图像去噪的信噪比。

  19. 基于峰值信噪比和小波方向特性的图像奇异值去噪技术%Image SVD denoising based on PSNR and wavelet directional feature

    Institute of Scientific and Technical Information of China (English)

    王敏; 周磊; 周树道; 叶松

    2013-01-01

    提出一种利用小波变换子图像不同的方向特性和峰值信噪比进行奇异值分解的图像去噪算法.由于图像经过小波变换后,低频子图像集中了原图像的大部分能量噪声,故仅作简单维纳滤波;而噪声则主要集中在小波域中的三个不同方向的高频子图中,且系数较小,因此可以利用奇异值分解进行去噪处理,即用较大的奇异值和对应的特征向量重构出去噪图像,然而由于奇异值分解固有的行列方向性,对于高频对角线子图重构出的图像去噪效果不理想,故采取旋转至行列方向后再进行常用的奇异值滤波;最后将去噪后的低频和高频子图进行小波反变换重构出最终的去噪图像,其中重构所需的奇异值个数由图像的峰值信噪比确定.实验结果表明,该方法在有效去噪的同时较好的保留了原有的高频细节信息.%An optimized image singular value decomposition (SVD) denoising algorithm based on wavelet transform directional information and peak signal to noise ratio (PSNR) was proposed. As most of the energy noises were concentrated in low-frequency sub-image after wavelet transform, the simple Wiener filtering was made; on the other hand, the image noises were mainly concentrated in the high-frequency sub-image with three different directions and the coefficient was smaller, so the larger singular values of SVD and their corresponding eigenvectors were used to reconstruct denoising image; however, because of the inherent directional feature of the SVD, the denoising result of image reconstructed from high-frequency diagonal sub-image was not satisfied, so the diagonal sub-image was rotated to the level (vertical) direction, then the SVD filtering was done; finally, the anti-wavelet transform was used to reconstruct the denoising image based on the low-frequency and high-frequency sub-images, and the required number of singular values was determined by PSNR of the image

  20. Novel color image denoising method based on fractional-order partial differential equation%基于分数阶偏微分方程的彩色图像去噪新方法

    Institute of Scientific and Technical Information of China (English)

    张富平; 周尚波; 赵灿

    2013-01-01

    The traditional methods for color image denoising usually can' t smooth the edge noise effectively and can' t protect the texture information well. To overcome these shortcomings, this paper proposed a novel method for color image denoising which combined DQFT with the theory of fractional order differentiation. The method used a quaternion matrix to represent a color image, and it was carried out by following procedures. Firstly, transformed the quaternion matrix to the DQFT domain. Secondly, applied it to the process of a fractional-order-differentiation-based energy function to seek its minimum, used the variational principle to solve this minimization problem and then inferred the proposed method. Finally, compared the method with two traditional methods for color image denoising to evaluate its superiority. The experimental results show that the proposed method has better performance in denoising and protection of the details.%为了克服现有彩色图像去噪方法不能有效抑制边缘噪声和保持纹理信息的缺点,提出了一种结合了离散四元数傅里叶变换(DQFT)和分数阶微分理论的彩色图像去噪改进方法.算法采用四元数矩阵表示一幅彩色图像,首先对该四元数矩阵进行离散傅里叶变换;然后将其代入基于分数阶微分的能量泛函极小值求解过程中,利用变分原理求解并推导出去噪模型,并与两种传统彩色图像去噪模型进行实验比较.实验结果表明,提出的模型在去噪效果和纹理保护方面都有更好的表现.

  1. Agricultural image de-noising algorithm based on hybrid wavelet transform%基于杂交小波变换的农产品图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    杨福增; 田艳娜; 杨亮亮; 何金伊

    2011-01-01

    The current image de-noising methods cannot remove the noise effectively, and they have the disadvantage of losing minutiae easily.A new de-noising method based on Hybrid Wavelet Transform was proposed in this study.Wavelet de-noising has the advantage of keeping the image's detail information, and Wiener Filter can obtain an optimal solution.This algorithm synthesized the advantages of Wavelet de-noising and Wiener Filter.Firstly, the image de-noised by Wavelet was used as male parent of the Hybrid Wavelet Transform's initial population, and image de-noised by Wiener Filter as female parent.Then, the individuals with fitness function of maximum between-cluster variance were evaluated.Through the hybrid and mutate operation, the gene recombination was realized, and then the superior gene of the two images de-noised was extracted by Wavelet and Wiener Filter.Finally, with the finite order hereditary algebra, an offspring image was obtained which has both advantages of male parent and female parent.The performance of this algorithm was tested by the red jujube images and wheat images.The results showed that images of red jujube and wheat de-noised by the proposed method had a higher PSNR (178.44 and 183.24) than those processed by conventional methodssuch as neighborhood average (176.76 and 175.16), median filter (174.79 and 173.13), Wiener filter (172.75 and 173.48) and Gauss filter (167.50 and 165.60) etc.The experimental results showed that the Hybrid Wavelet Transform de-noising method used on agricultural image had the advantage of high signal-to-noise ratio, and good visual effect.Therefore, the method proposed is effective and practicable.%针对现有图像去噪方法去噪效果不明显、易丢失细节特征等缺陷,提出了一种基于杂交小波变换的农产品图像去噪算法.该方法综合了小波去噪能较好保留图像细节特征和Wiener滤波器可得到最优解的优势,分别以经小波变换、Wiener滤波处理后的图像作

  2. Hybrid Denoising Method for Removal of Mixed Noise in Medical Images

    Directory of Open Access Journals (Sweden)

    J UMAMAHESWARI, Dr.G.RADHAMANI

    2012-05-01

    Full Text Available Nowadays, Digital image acquisition and processing techniques plays a very important role in current day medical diagnosis. During the acquisition process, there could be distortions in the images, which will negatively affect the diagnosis images. In this paper a new technique based on the hybridization of wavelet filter and center weighted median filters is proposed for denoising multiple noise (Gaussian and Impulse images. The model is experimented on standard Digital Imaging and Communications in Medicine (DICOM images and the performances are evaluated in terms of peak signal to noise ratio (PSNR, Mean Absolute Error (MAE, Universal Image Quality Index (UQI and Evaluation Time (ET. Results prove that utilization of center weighted median filters in combination with wavelet thresholding filters on DICOM images deteriorates the performance. The proposed filter gives suitable results on the basis of PSNR, MSE, UQI and ET. In addition, the proposed filter gives nearly uniform and consistent results on all the test images.

  3. 基于小波阈值压缩的运动模糊图像去噪算法%Motion Fuzzy Image Denoising Algorithm Based on Wavelet Threshold Compression

    Institute of Scientific and Technical Information of China (English)

    李敏; 郭磊

    2015-01-01

    Under the condition of strong interference, the fuzzy motion picture usually contains a lot of noise, it is difficult to carry out the detailed analysis, and it is necessary to carry out the image noise reduction filtering. The traditional method us-es wavelet analysis to reduce the noise of the image details. A new image denoising algorithm based on wavelet threshold compression is proposed. To construct a fuzzy wavelet image motion analysis model, using wavelet threshold compression method of motion blurred image corner detection, forming with corner layer wavelet threshold compression library, to real-ize filter for image denoising processing. Simulation results show that the using the algorithm can effectively achieve the im-age denoising and improve the image quality and peak signal-to-noise ratio.%强干扰环境下采集的模糊运动图像通常含有大量的噪声,难以进行有效的细节分析,需要进行图像降噪滤波处理.传统方法采用小波分析的图像细节滤波算法进行降噪,对运动场景下的图像角点偏移部分的降噪效果不好.提出一种基于小波阈值压缩的运动模糊图像去噪算法.构建模糊运动图像的小波分析模型,采用小波阈值压缩方法进行运动模糊图像的角点检测,形成具有角点的图层小波阈值压缩库,实现图像降噪滤波处理.仿真结果表明,采用该算法能有效实现图像去噪滤波,提高图像质量和峰值信噪比.

  4. A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Merlin, Thibaut, E-mail: thibaut.merlin@telecom-bretagne.eu [Université Bordeaux INCIA, CNRS UMR 5287, Hôpital de Bordeaux , Bordeaux 33 33076 (France); Visvikis, Dimitris [INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, Brest 29 29609 (France); Fernandez, Philippe; Lamare, Frederic [Université Bordeaux INCIA, CNRS UMR 5287, Hôpital de Bordeaux, Bordeaux 33 33076 (France)

    2015-02-15

    Purpose: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy–Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. Methods: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy–Richardson deconvolution algorithm to the current estimation of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Results: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. Conclusions: This study demonstrates the feasibility of incorporating the Lucy–Richardson deconvolution associated with a

  5. 基于偏微分方程的常用图像去噪方法比较研究%Based on Partial Differential Equations of the Comparative Study of Common Image Denoising

    Institute of Scientific and Technical Information of China (English)

    沈永顺

    2011-01-01

    In image processing,image denoising extremely important.In this paper,methods commonly used in image denoising a detailed overview of the methods described and their advantages and disadvantages of theoretical principles,but also gives its vice-test on the same image denoising effect.%图像去噪在图像处理中极其重要。本文就几种常用的图像去噪方法进行了详细的综述,阐述了各方法的理论原理以及其优缺点,同时也给出了其对同一副测试图像的去噪效果。

  6. Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity.

    Science.gov (United States)

    Xiong, Ruiqin; Liu, Hangfan; Zhang, Xinfeng; Zhang, Jian; Ma, Siwei; Wu, Feng; Gao, Wen

    2016-09-27

    This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively-learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.

  7. Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

    Science.gov (United States)

    Ragheb, Amr M.; Osman, Heba; Abbas, Alaa M.; Elkaffas, Saleh M.; El-Tobely, Tarek A.; Khamis, S.; Elhalawany, Mohamed E.; Nasr, Mohamed E.; Dessouky, Moawad I.; Al-Nuaimy, Waleed; Abd El-Samie, Fathi E.

    2012-12-01

    To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity-hue-saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.

  8. 基于自适应过完备稀疏表示的红外图像滤波方法%Infrared Image Denoising Based on Adaptive Over-complete Sparse Representation

    Institute of Scientific and Technical Information of China (English)

    代少升; 李美玲

    2012-01-01

    As traditional infrared image denoising methods usually achieve the satisfactory effect at the cost of damaging the image content,an infrared image denoising method based on adaptive over-complete sparse representation is presented.The image can be decomposed via orthogonal matching pursuit algorithm into the content part and the remainer on the adaptive dictionary trained by the K-SVD method in allusion to the infrared image,eventually the infrared image can be reconstructed by the content part.Experimental results show the effectiveness of the method.%针对传统滤波算法在滤除红外图像噪声时会损失部分有用信息的问题,提出一种基于自适应过完备稀疏表示的红外图像滤波方法。该方法采用K-SVD算法以待滤波的红外图像为样本训练出自适应过完备原子库;采用正交匹配跟踪算法将红外图像信号在该过完备原子库上稀疏分解为稀疏成分和其他成分,稀疏成分对应红外图像中的有用信息,其他成分对应红外图像中的噪声,由稀疏成分重建图像,从而达到消除噪声的目的。实验结果表明:该方法相比传统方法具有更好的滤波效果,重建图像质量较高。

  9. Image de-noising method based on HMT model interlayer mapping in wavelet domain%基于HMT模型层间映射的图像邻域去噪算法

    Institute of Scientific and Technical Information of China (English)

    宫霄霖; 毛瑞全

    2011-01-01

    A new image de-noising algorithm based on image segmentation is proposed to keep image edges more effectively.The proposed method segments the low-frequency subband into many domains adaptively by PCNN, and treats the connected regrions gotten as neighbourhood. With a simplified HMT model in both discrete and stationary wavelet, the quad-tree inter-layer model is used to map the neighbourhood into the high-frequency subbands. And the regions gotten are taken as irregular neighborhoods for denoising.Further it chooses coefficients both in irregular neighborhood and in a fixed rectangular window, and selects the coefficients have closer geometric distance. A better restoration of images is demonstrated in the results of experiments, with detail of images kept as well as image noises decreasing.%本文提出了一种以图像分割为基础的图像去噪算法.本文算法根据图像自身的性质,利用脉冲耦合神经网络模型自适应地将小波分解后的低频图像分割成不同的区域,并且利用简化的HMT层间模型在离散和平稳小波分别处理的情况下,将得到的连通区域邻域映射到各个不同的高频子带上.进一步结合固定的窗口,作为邻域去噪算法中的邻域.实验结果表明,该方法在降低了图像噪声的同时又尽可能地保留了图像的边缘信息,是一种有效的去噪方法.

  10. Research on image denoising based on median filter and contourlet transform%基于中值滤波和Contourlet变换的图像去噪研究

    Institute of Scientific and Technical Information of China (English)

    李万臣; 赵开伟

    2011-01-01

    针对图像中同时含有脉冲噪声和高斯噪声的情况,提出了一种中值滤波和Contourlet变换相结合的图像去噪方法,首先用中值滤波检测出脉冲噪声的噪声点并加以处理,然后用Contourlet变换对高斯噪声进行处理.实验结果表明,此方法不仅能有效地滤除脉冲和高斯的混合噪声,提高去噪后图像的PSNR值,而且可以很好地保留图像的细节信息,改善视觉效果.%For the image containing both impulse noise and Gaussian noise, this paper presented a image denoising method which combined median filter with contourlet transform. The noise point of impulse noise was detected and processed with median filter; the Gaussian noise was processed with contourlet transform. Experimental results showed that the method not only could remove mixed impulse and Gaussian noise effectively, but also could get higher PSNR value and better visual effect compared with other methods.

  11. Semi-implicit Image Denoising Algorithm for Different Boundary Conditions

    Directory of Open Access Journals (Sweden)

    Yuying Shi

    2013-04-01

    Full Text Available In this paper, the Crank-Nicolson semi-implicit difference scheme in matrix form is applied to discrete the Rudin-Osher-Fatemi model. We also consider different boundary conditions: Dirichlet boundary conditions, periodic boundary conditions, Neumann boundary conditions, antireflective boundary conditions and mean boundary conditions. By comparing the experimental results of Crank-Nicolson semi-implicit scheme and explicit scheme with the proposed boundary conditions, we can get that the semi-implicit scheme can overcome the instability and the number of iterations of the shortcomings that the explicit discrete scheme has, and its recovery effects are better than the explicit discrete scheme. In addition, the antireflective boundary conditions and Neumann boundary conditions can better maintain the continuity of the boundary in image denoising.

  12. 图像小波去噪的算子描述%Operator Description of Image Wavelet Denoising

    Institute of Scientific and Technical Information of China (English)

    林克正; 李殿璞; 华克强

    2000-01-01

    The scheme of image denoising based on two-dimensional discrete wavelet transform is suggested.The denoising algorithm is described with some operators. By thresholding the wave lettransform coefficients of noisy images,the original image can be reconstructed correctly.Different threshold selection and thresholding method sared is cussed.A new adaptive local threshold scheme is proposed.Quantifying the perfor mance of image denoising schemes by using the mean square error,the performance of the adaptivelocal thresholds chemeisdemon trated,and compare with theuni versal threshold scheme.The experiment shows that image denoising using the former schemeper forms better than the one using the latter scheme.%给出了一种基于二维离散小波变换的图像去噪方法,并用算子的形式加以描述,通过对小波变换系数进行阈值处理实现图像的去噪。讨论了不同的阈值选取方法 和阈值策略,并提出了一种自适应局部阈值法。用均方差衡量去噪性能,实验结果证明:用自适应局部阈值法去噪好于全局阈值法去噪。

  13. Electrocardiogram signal denoising based on a new improved wavelet thresholding

    Science.gov (United States)

    Han, Guoqiang; Xu, Zhijun

    2016-08-01

    Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

  14. Using anisotropic diffusion equations in pixon domain for image de-noising

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren; Sharifzadeh, Sara

    2013-01-01

    Image enhancement is an essential phase in many image processing algorithms. In any image de-noising algorithm, it is a major concern to keep the interesting structures of the image. Such interesting structures in an image often correspond to the discontinuities in the image (edges). In this paper......, we propose a new algorithm for image de-noising using anisotropic diffusion equations in pixon domain. In this approach, diffusion equations are applied on the pixonal model of the image. The algorithm has been examined on a variety of standard images and the performance has been compared...... with algorithms known from the literature. The experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in de-noising and preserving image edges....

  15. Dictionary learning based denoising of low-dose X-ray CT image%基于字典学习的低剂量X-ray CT图像去噪

    Institute of Scientific and Technical Information of China (English)

    朱永成; 陈阳; 罗立民; Toumoulin Christine

    2012-01-01

    介绍了一种基于字典学习的去噪方法,并将其应用于降低低剂量CT图像噪声水平的研究.针对体模图像和病人图像,分别选择低剂量CT图像和正常剂量CT图像作为训练样本,采用K-SVD算法,通过迭代学习构建图像字典;然后,结合正交匹配跟踪算法,实现图像稀疏表示,稀疏成分对应于图像的有用信息,其他成分对应于图像噪声;最后,依据图像的稀疏成分重建图像,达到去除噪声的目的.实验结果表明:字典的大小、稀疏表示的约束条件等参数会显著影响所提算法的去噪结果;相比低剂量CT图像,将正常剂量CT图像作为训练样本可以得到更好的去噪结果;在相同的噪声水平下,所提算法与传统图像去噪算法相比可以更好地去除图像噪声,且保留了图像的细节信息.%A dictionary learning based denoising method is introduced to eliminate the noise in low-dose computed-tomography (LDCT) image. Aiming at the phantom and patient images, the &-means singular value decomposition (K-SVD) algorithm is adopted to train image dictionary itera-tively based on LDCT and normal-dose CT (NDCT) images. Then, based on the orthogonal matching pursuit algorithm, the sparse representation decomposes the noise image into sparse component which is related to image information and remains which are regarded as noise. Finally, noises can be suppressed by reconstructing image only with its sparse components. The experimental results show that the performance of the proposed method is strongly affected by the dictionary size and the constraints for sparsity in dictionary training. The better performance can be achieved when training samples are extracted from NDCT image instead of LDCT image. Under the same noise level, compared with the traditional de-noising methods, the proposed method is more effective in suppressing noise and improving the visual effect while maintaining more diagnostic image details.

  16. 一种基于非局部思想的改进图像降噪算法%Improved image denoising algorithm based on non-local idea

    Institute of Scientific and Technical Information of China (English)

    刘苒苒; 武小平; 韦超; 孔泽伦

    2016-01-01

    在基于稀疏和冗余字典的图像降噪算法的基础上,提出了一种基于非局部思想的改进图像降噪算法。与传统的基于稀疏表达的图像降噪算法K-SVD相比,提出的算法增加了一个相似块聚合的过程,使得学习的字典更小且更准确。利用自然图像包含很多的自相似,相似样本聚合学习出的字典比传统K-SVD算法能更准确更稀疏地表示样本。稀疏度的提高使得重建后的信号更加准确、适应性更好。实验证明提出的算法取得了更好的视觉效果。%This paper presented an improved image denoising method based on sparse and redundant representations over trained dictionaries.With the traditional image denoising algorithm based on sparse expression compared to the K-SVD algo-rithm,it added a similar block polymerization process to build the smaller and more accurate learning dictionary.The novel idea behind the proposed approach was that natural images contained so many self-similarities that signal sparsity could be further promoted.This sparsity promotion made the learned dictionary more adaptive and accurate to restore the signal.The experimen-tal results demonstrate that this proposed method provides better visual quality compared to the state-of-the-art methods.

  17. Blind Analysis of CT Image Noise Using Residual Denoised Images

    CERN Document Server

    Roychowdhury, Sohini; Alessio, Adam

    2016-01-01

    CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implement and evaluate the noise estimation performance of six spatial- and frequency- based methods, derived from conventional image filtering algorithms. Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region. Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance. The proposed automated methods tend to underestimate CT image noise at low-flux levels...

  18. A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image.

    Science.gov (United States)

    Wang, Fei; Wang, Yibin; Yang, Meng; Zhang, Xuetao; Zheng, Nanning

    2017-01-26

    An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

  19. A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-01-01

    Full Text Available An Intensified Charge-Coupled Device (ICCD image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a Different from the independent identically distributed (i.i.d. noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

  20. An algorithm for 252Cf-Source-Driven neutron signal denoising based on Compressive Sensing

    Institute of Scientific and Technical Information of China (English)

    李鹏程; 魏彪; 冯鹏; 何鹏; 米德伶

    2015-01-01

    As photoelectrically detected 252Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition (EMD) is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function (IMF) automatically. Thus, we only need to use the basis pursuit denoising (BPDN) algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN (Empirical Mode Decomposition Compressive Sensing Denoising). In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.

  1. An Adaptive Image Denoising Model of Anisotropic Diffusion Based on Fractional Derivative%基于分数阶导数的自适应各向异性扩散图像去噪模型

    Institute of Scientific and Technical Information of China (English)

    杨迎春; 桂志国; 李化奇; 李晓岩

    2011-01-01

    针对传统的纯各向异性扩散模型(一阶导数,用梯度表示)在平滑区域过度扩散,产生“阶梯效应”和四阶PDE(Partial Differential Equations)模型(二阶导数,用Laplace算子表示)去噪效果差的缺点,在分数阶偏微分理论的基础上提出了基于分数阶导数的自适应各向异性扩散图像去噪模型.该模型在图像的不同位置采用不同的正则化约束,具有局部自适应的特点.实验结果表明:该模型在有效去除噪声的同时,能够很好地保持图像的边缘和纹理细节信息,经过该算法处理后的图像具有更好的质量和视觉效果.%As the traditional pure anisotropic diffusion model (1-order derivative used by the gradient) brings "staircase effect" by excessive diffusion in smooth regions, and the 4-order PDE (2-order derivative used by the Laplacian) model suffers poor denoising effect, an adaptive image denoising model of anisotropic diffusion based on fractional derivative was proposed. As a locally adaptive process, the proposed model adopts different regularization constraints in different parts of the image. Experimental results show that the new model not only efficiently remove noise, but also retain the edge and detail information. Better quality and visual effects of the image is achieved with this model.

  2. Two-stage Image Denoising Using Patch-based Singular Value Decomposition%基于分块奇异值分解的两级图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    刘涵; 梁莉莉; 黄令帅

    2015-01-01

    为了更有效地进行图像去噪,提出了一种基于分块奇异值分解(Singular value decomposition, SVD)的两级图像去噪方法,该方法首先将含噪图像中具有相似结构的图像块组织成具有很强相关性的图像块组;然后,利用二维奇异值分解去除图像块组中每个相似块的内部相关性,利用一维奇异值分解去除相似图像块组之间的冗余;最后,通过硬阈值方法收缩变换系数实现图像与噪声的有效分离。为了进一步提高去噪效果,对含噪图像再次进行上述操作。不同的是,在第二级去噪过程中,相似图像块组根据第一级估计出的图像计算获得且相似图像块间的相关性通过离散余弦变换去除。仿真实验表明,提出的两级图像去噪算法不仅可以较大程度地去除图像噪声,还能有效保留图像细节,取得了良好的去噪效果。%This paper presents an efficient patch-based image denoising scheme by using singular value decomposition (SVD). In this scheme, similar image patches from a noisy image are simply grouped together. For a better sparse representation of these similar patches, firstly, the 2-D SVD is utilized to reveal the essential features of each individual patch, and then the 1-D SVD is utilized to exploit the correlation between similar patches. By doing so, the image features can be well preserved when attenuating the noise by the shrinkage of transform co-efficients. To further improve the denoising performance, the proposed scheme is employed once again. But the similar patch grouping is performed from the first-stage estimated image and a fixed orthogonal transform instead of 1-D SVD is adopted to remove the redundancy shared by similar patches. Experimen-tal results show that the proposed two-stage denoising scheme achieves more competitive performance than the state-of-the-art denoising algorithms, especially in preserving image details and introducing very few artifacts.

  3. Similarity-based denoising of point-sampled surface

    Institute of Scientific and Technical Information of China (English)

    Ren-fang WANG; Wen-zhi CHEN; San-yuan ZHANG; Yin ZHANG; Xiu-zi YE

    2008-01-01

    A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the differential signal of each sample point is determined and called "geometry intensity". Based on covariance analysis, a regular grid of geometry intensity of a sample point is constructed, and the geometry-intensity similarity of two points is measured according to their grids. Based on mean shift clustering, the PSSs are clustered in terms of the local geometry-features similarity. The smoothed geometry intensity, i.e., offset distance, of the sample point is estimated according to the two similarities. Using the resulting intensity, the noise component from PSSs is finally removed by adjusting the position of each sample point along its own normal direction. Experimental results demonstrate that the algorithm is robust and can produce a more accurate denoising result while having better feature preservation.

  4. Vertex-based diffusion for 3-D mesh denoising.

    Science.gov (United States)

    Zhang, Ying; Ben Hamza, A

    2007-04-01

    We present a vertex-based diffusion for 3-D mesh denoising by solving a nonlinear discrete partial differential equation. The core idea behind our proposed technique is to use geometric insight in helping construct an efficient and fast 3-D mesh smoothing strategy to fully preserve the geometric structure of the data. Illustrating experimental results demonstrate a much improved performance of the proposed approach in comparison with existing methods currently used in 3-D mesh smoothing.

  5. A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing

    NARCIS (Netherlands)

    Preusser, T.; Droske, M.; Garbe, C. S.; Telea, A.; Rumpf, M.

    2007-01-01

    The estimation of optical flow fields from image sequences is incorporated in a Mumford-Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set

  6. Edge-preserving image denoising via group coordinate descent on the GPU.

    Science.gov (United States)

    McGaffin, Madison Gray; Fessler, Jeffrey A

    2015-04-01

    Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.

  7. Point Set Denoising Using Bootstrap-Based Radial Basis Function

    Science.gov (United States)

    Ramli, Ahmad; Abd. Majid, Ahmad

    2016-01-01

    This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. PMID:27315105

  8. Image Denoising Algorithm Using Second Generation Wavelet Transformation and Principle Component Analysis

    Directory of Open Access Journals (Sweden)

    Asem Khmag

    2014-07-01

    Full Text Available This study proposes novel image denoising algorithm using combination method. This method combines both Wavelet Based Denoising (WBD and Principle Component Analysis (PCA to increase the superiority of the observed image, subjectively and objectively. We exploit the important property of second generation WBD and PCA to increase the performance of our designed filter. One of the main advantages of the second generation wavelet transformation in noise reduction is its ability to keep the signal energy in small amount of coefficients in the wavelet domain. On the other hand, one of the main features of PCA is that the energy of the signal concentrates on a very few subclasses in PCA domain, while the noise’s energy equally spreads over the entire signal; this characteristic helps us to isolate the noise perfectly. Our algorithm compares favorably against several state-of-the-art filtering systems algorithms, such as Contourlet soft thresholding, Scale mixture by WT, Sparse 3D transformation and Normal shrink. In addition, the combined algorithm achieves very competitive performance compared with the traditional algorithms, especially when it comes to investigating the problem of how to preserve the fine structure of the tested image and in terms of the computational complexity reduction as well.

  9. A Denoising Algorithm of Image for UAV Based on Wavelet Transform and Mean-value Filtering%基于小波变换和中值滤波的无人机图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    陈晓; 徐家品

    2011-01-01

    The unmanned aerial image in real-time transmission process, there may be mixed by both impulsive and Gaussian noise pollution, for the subsequent identification of the image caused great difficulties. In response, this paper based on median filter and wavelet transform for image denoising. Simulation results show that the method can effectively filter and Gaussian mixture impulsive noise, and can keep a good image detail, to improve the image of the visual effects.%在无人机航拍图像的实时传输过程中,有可能会同时受到脉冲和高斯混合噪声的污染,为后续图像的识别造成很大的困难.针对这种情况,提出了一种基于中值滤波和小波变换相结合的图像去噪方法.仿真结果表明,该方法不仅能有效地滤除脉冲和高斯的混合噪声,而且可以很好地保留图像的细节信息,改善图像的视觉效果.

  10. Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain.

    Science.gov (United States)

    Pang, Jiahao; Cheung, Gene

    2017-04-01

    Inverse imaging problems are inherently underdetermined, and hence, it is important to employ appropriate image priors for regularization. One recent popular prior-the graph Laplacian regularizer-assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper, we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising. Specifically, we first show the convergence of the graph Laplacian regularizer to a continuous-domain functional, integrating a norm measured in a locally adaptive metric space. Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain. We then interpret graph Laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations, e.g., its tendency to promote piecewise smooth signals under certain settings. To verify our analysis, an iterative image denoising algorithm is developed. Experimental results show that our algorithm performs competitively with state-of-the-art denoising methods, such as BM3D for natural images, and outperforms them significantly for piecewise smooth images.

  11. Airport runway radar image de-noising based on 2-D shift-invariance hybrid transform%基于移不变二维混合变换的机场雷达噪声抑制

    Institute of Scientific and Technical Information of China (English)

    刘帅奇; 胡绍海; 肖扬; 赵杰; 刘秀玲

    2015-01-01

    Foreign object debris (FOD)detection in airport runway is very important to airplanes′safety, and the airport runway radar image noise suppressing plays a vital role in foreign object detection.Therefore,an airport runway radar image de-noising method based shift invariant hybrid transform is proposed in the range-time dimension.Firstly,the radar image noise in the range dimension is removed by the Wiener filter in discrete Fourier transform(DFT)domain.Secondly,the radar image noise in the time dimension is removed by the adap-tive threshold in hyperanalytic wavelet transform(HWT)domain.Compared with traditional de-noising methods af-ter imaging,the proposed mothod can remove the runway radar image noise effectively and improve the visual effect of images significantly,and most importantly,it can run in real time and be suitable for engineering practice.%机场跑道异物(foreign object debris,FOD)检测对飞行器的安全起降有着非常重要的意义,而机场跑道异物检测的一个关键环节是很好地抑制机场雷达图像的噪声,因此提出一种基于距离时间维的移不变混合变换以抑制机场雷达图像的噪声。首先,在雷达成像时进行离散傅里叶变换(discrete Fourier transform,DFT)和维纳滤波滤除距离维上的噪声。然后,在雷达成像时进行超分析离散小波变换(hyperanalytic wavelet transform, HWT)自适应滤波去除时间维上的噪声。与传统的成像后去噪算法相比,本文的算法可以有效地去除机场雷达图像噪声,显著地改善图像的视觉效果。最重要的是该算法具有很强的实时性,可以很好地应用到工程实践中。

  12. A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE

    Directory of Open Access Journals (Sweden)

    Yang Fan

    2012-10-01

    Full Text Available Abstract Background Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL and Radio-Frequency Ablation (RFA of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention. Methods In this paper, we proposed a novel framework for kidney segmentation of ultrasound (US images combined with nonlocal total variation (NLTV image denoising, distance regularized level set evolution (DRLSE and shape prior. Firstly, a denoised US image was obtained by NLTV image denoising. Secondly, DRLSE was applied in the kidney segmentation to get binary image. In this case, black and white region represented the kidney and the background respectively. The last stage is that the shape prior was applied to get a shape with the smooth boundary from the kidney shape space, which was used to optimize the segmentation result of the second step. The alignment model was used occasionally to enlarge the shape space in order to increase segmentation accuracy. Experimental results on both synthetic images and US data are given to demonstrate the effectiveness and accuracy of the proposed algorithm. Results We applied our segmentation framework on synthetic and real US images to demonstrate the better segmentation results of our method. From the qualitative results, the experiment results show that the segmentation results are much closer to the manual segmentations. The sensitivity (SN, specificity (SP and positive predictive value

  13. Denoising of PET images by context modelling using local neighbourhood correlation

    Science.gov (United States)

    Huerga, Carlos; Castro, Pablo; Corredoira, Eva; Coronado, Monica; Delgado, Victor; Guibelalde, Eduardo

    2017-01-01

    Positron emission tomography (PET) images are characterised by low signal-to-noise ratio and blurred edges when compared with other image modalities. It is therefore advisable to use noise reduction methods for qualitative and quantitative analyses. Given the importance of the maximum and mean uptake values, it is necessary to avoid signal loss, which could modify the clinical significance. This paper proposes a method of non-linear image denoising for PET. It is based on spatially adaptive wavelet-shrinkage and uses context modelling, which explicitly considers the correlation between neighbouring pixels. This context modelling is able to maintain the uptake values and preserve the edges in significant regions. The algorithm is proposed as an alternative to the usual filtering that is performed after reconstruction.

  14. 基于稀疏表示和字典学习的QR码图像去噪%QR code image denoising based on sparse representation and dictionary learning

    Institute of Scientific and Technical Information of China (English)

    孙道达; 赵健; 王瑞; 冯宁; 胡江华

    2012-01-01

    目的 针对噪声对QR码图像识别干扰,提出一种基于稀疏表示和字典学习的自适应去噪算法.方法 采用稀疏表示和字典学习的方法.结果 得到高效描述图像内容的字典,能更有效地滤除图像中的噪声,保留原图像的有用信息.结论 利于QR码的准确、快速识别,可大大提高识别率.%Aim An adaptive denoising algorithm is proposed based on sparse representation and dictionary learning for the noise interference on QR code image recognition. Methods The method of sparse representation and dictionary learning is used. Results A dictionary which has an efficient description of image content is obtained. The results show that the algorithm can filter out the noise in the image more effectively and retain the useful information of the original image. Conclusion The algorithm is conducive to the accurate and rapid recognition of the QR code, and greatly improve the recognition rate.

  15. DENOISING METHOD BASED ON SINGULAR SPECTRUM ANALYSIS AND ITS APPLICATIONS IN CALCULATION OF MAXIMAL LIAPUNOV EXPONENT

    Institute of Scientific and Technical Information of China (English)

    LIU Yuan-feng; ZHAO Mei

    2005-01-01

    An algorithm based on the data-adaptive filtering characteristics of singular spectrum analysis (SSA) is proposed to denoise chaotic data. Firstly, the empirical orthogonal functions ( EOFs ) and principal components ( PCs ) of the signal were calculated, reconstruct the signal using the EOFs and PCs, and choose the optimal reconstructing order based on sigular spectrum to obtain the denoised signal. The noise of the signal can influence the calculating precision of maximal Liapunov exponents. The proposed denoising algorithm was applied to the maximal Liapunov exponents calculations of two chaotic system, Henon map and Logistic map. Some numerical results show that this denoising algorithm could improve the calculating precision of maximal Liapunov exponent.

  16. Image denoising with improved bivariate model based on uniform discrete curvelet transform%基于 UDCT 的改进双变量模型图像去噪

    Institute of Scientific and Technical Information of China (English)

    杨兴明; 牛坡礼

    2015-01-01

    By researching on the statistical properties of wavelet coefficients of the uniform discrete curvelet transform(UDCT ) domain ,a new denoising algorithm based on the bivariate model is proposed to make up for the unconsideration of spatial clustering in traditional bivariate model .Firstly ,the Monte Carlo method is used to estimate the noise variance of each subband on the basis of the bivariate model .Secondly ,in order to obtain the initial image ,the neighborhood model is introduced to estimate the measurement variance of the wavelet coefficients of corresponding window by adjusting the size of neighborhood window .Finally ,the ini‐tial image and the original noising image are used as prior information to deduce the improved bivariate model for dealing with the original noising image .And the final denoising image is acquired under the convergence condition of symmetric Kullback‐Leibler divergence and the maximum number of iterations .The experimental results prove the effectiveness of this method .%文章通过对均匀离散曲波变换(U DC T )域中小波系数统计特性的研究,针对传统双变量模型未考虑空间聚集性的不足,提出了一种新的双变量模型去噪算法。首先在双变量模型的基础上采用了蒙特卡洛方法估计各子带的噪声方差;然后引入邻域模型,通过调整邻域窗的大小估计相应窗口内小波系数的度量方差,得到初始化图像;最后以初始化图像和原噪声图像为先验信息,推导出改进的双变量模型来处理原噪声图像,且以对称K‐L散度和最大迭代次数为收敛条件,得到最终去噪图像。实验结果证明了该算法的有效性。

  17. Study on Image Denoising Method Based On Curvelet Transform and Weighted Mean Filter%基于 Curvelet 变换和均值滤波的图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    陈木生

    2014-01-01

    The purpose of this paper is to study a method of de-noising of images corrupted with additive white Gaussian noise.Firstly,the noisy image is decomposed into many levels to obtain different frequency sub-bands by Curvelet transform.Secondly,the threshold estimation and the weighted average method are used to remove the noisy coefficients according to generalized Gaussian distribution modeling of sub-band coefficients.Ultimately,invert the multi-scale decomposition to re-construct the de-noised image.Here,to prove the performance of the proposed method,the results are compared with other existent algorithms such as hard and soft threshold based on wavelet.The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that Curvelet transform can achieve a better performance than the wavelet transform in image de-noising.%针对图像中的高斯噪声干扰,提出一种改进的图像去噪方法。首先利用 Curvelet 变换将含噪声图像分解成多个子频带,再根据子带系数的高斯分布特性,利用阈值去噪和加权平均滤波相结合的方法对高频子带进行去噪处理,最后利用 Curvelet 反变换得到去噪后的图像。为了验证该方法的有效性,与传统的硬阈值、软阈值、基于小波变换的方法相比较,实验结果表明,该方法能够获得较好的峰值信噪比和视觉特性,保留较多的细节信息。同时也说明了 Curvelet 变换比小波变换能够得到更好的去噪效果。

  18. NOTE: Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction

    Science.gov (United States)

    Holan, Scott H.; Viator, John A.

    2008-06-01

    Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

  19. Novel Image Denoising Method Based on Discrete Fractional Orthogonal Wavelet Transform%基于离散分数阶正交小波变换图像降噪新方法

    Institute of Scientific and Technical Information of China (English)

    徐小军; 王友仁

    2014-01-01

    分数阶小波变换是小波变换时间-频域的分析方法在时间-分数阶频率域的推广,在时间和分数阶频率域具有表征信号特征的能力。本文在离散分数阶正交小波变换(DFRWT )多分辨率分析(MRA )理论基础上,推导出DFRWT系数分解及重构新形式并作二维扩展。根据图像DFRWT子带系数能量随不同阶数p变化的特点,提出基于DFRWT阈值降噪新方法。该方法在保持子带低频能量为绝对大值条件下,适当提高子带高频能量值,更利于抑制图像噪声。实验结果表明,与传统小波阈值降噪方法相比,该方法主观质量得到了明显增强,提高了峰值信噪比。%The fractional wavelet transform extend the analysis method of wavelet transform about time-frequency domain to time-fractional frequency domain ,can characterize signal features in time and fractional-frequency domain .Based on the theory of multiresolution analysis (MRA ) of the fractional wavelet ,the new forms about coefficient decomposition and reconstruction of dis-crete fractional wavelet transform (DFRWT ) are given and made two-dimensional expansion .According to the feature of subband coefficient energy of image based on DFRWT varies with different p order ,a novel image threshold denoising method based on DFRWT is proposed .The method in conditions of keeping the energy of low frequency subband for absolute great value ,appropriate to raise the distribution of energy percentage ,more beneficial for image noise suppression .The experimental results show that the performance of this method is better than the traditional wavelet threshold denoising method both in vision effect and peak signal to noise ratio .

  20. 3D MR image denoising using rough set and kernel PCA method.

    Science.gov (United States)

    Phophalia, Ashish; Mitra, Suman K

    2017-02-01

    In this paper, we have presented a two stage method, using kernel principal component analysis (KPCA) and rough set theory (RST), for denoising volumetric MRI data. A rough set theory (RST) based clustering technique has been used for voxel based processing. The method groups similar voxels (3D cubes) using class and edge information derived from noisy input. Each clusters thus formed now represented via basis vector. These vectors now projected into kernel space and PCA is performed in the feature space. This work is motivated by idea that under Rician noise MRI data may be non-linear and kernel mapping will help to define linear separator between these clusters/basis vectors thus used for image denoising. We have further investigated various kernels for Rician noise for different noise levels. The best kernel is then selected on the performance basis over PSNR and structure similarity (SSIM) measures. The work has been compared with state-of-the-art methods under various measures for synthetic and real databases.

  1. Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain

    Indian Academy of Sciences (India)

    Mourad Talbi

    2014-08-01

    In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application of the inverse of BWT (BWT−1) to the de-noise de-noised bionic wavelet coefficients. For evaluating this new proposed de-noising technique, we have compared it to a thresholding technique in the FWT_TI domain. Preliminary tests of the application of the two de-noising techniques were constructed on a number of ECG signals taken from MIT-BIH database. The obtained results from Signal to Noise Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed de-noising technique outperforms the second technique. We have also compared the proposed technique to the thresholding technique in the bionic wavelet domain and this comparison was performed by SNR improvement computing. The obtained results from this evaluation showed that the proposed technique also outperforms the de-noising technique based on bionic wavelet coefficients thresholding.

  2. Implementation of GPR Signals De-Noising Based on DSP

    Institute of Scientific and Technical Information of China (English)

    CHEN Xiao-li; TIAN Mao; ZHOU Hui-lin

    2005-01-01

    An important issue of ground-penetrating radar (GPR) signals analysis is de-noising that is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor (DSP) based on wavelet shrinkage algorithm. In order to realize real-time GPR signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimental results show that the DSP based application not only basically meets the real-time requirement of GPR signals analysis, but also assures the quality of the GPR signals analysis.

  3. Extreme value analysis of frame coefficients and implications for image denoising

    CERN Document Server

    Haltmeier, Markus

    2012-01-01

    Denoising by frame thresholding is one of the most basic and efficient methods for recovering a discrete signal or image from data that are corrupted by additive Gaussian white noise. The basic idea is to select a frame of analyzing elements that separates the data in few large coefficients due to the signal and many small coefficients mainly due to the noise $\\epsilon_n$. Removing all data coefficients being in magnitude below a certain threshold yields an approximation to the original signal. In order that a significant amount of the noise is removed and at the same time relevant information about the original image is kept, a precise understanding of the statistical properties of thresholding is important. For that purpose we compute, for the first time, the asymptotic distribution of $\\max_{\\om\\in\\Om_n} \\abs{\\inner{\\base_\\om^n, \

  4. Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2009-01-01

    Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de...

  5. Spatial and Transform Domain Filtering Method for Image De-noising: A Review

    Directory of Open Access Journals (Sweden)

    Vandana Roy

    2013-09-01

    Full Text Available Present investigation reveals the quantum of work carried in the filtering methods for image de-noising. An image is often gets corrupted by various noises that are visible or invisible while being gathered, coded, acquired and transmitted. Noise influences various process parameters that may cause a quality problem for further image processing. De-noising of natural images is appears to be very simple however when considered under practical situations becomes complex. It has been cited by various author that parameter such as type and quantum of noise, image etc. through single algorithm or approach becomes cumbersome when results are optimized. In order to improve the quality of an image noise must be removed when the image is pre-processed and the important signal features like edge details should be retained as much as possible. The search on efficient image de-noising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper reviews significant de-noising methods (spatial and transform domain method and their salient features and applications. One filter in each category has been taken in consideration to understand the characteristics of both spatial and transform domain filters.

  6. A computationally efficient denoising and hole-filling method for depth image enhancement

    Science.gov (United States)

    Liu, Soulan; Chen, Chen; Kehtarnavaz, Nasser

    2016-04-01

    Depth maps captured by Kinect depth cameras are being widely used for 3D action recognition. However, such images often appear noisy and contain missing pixels or black holes. This paper presents a computationally efficient method for both denoising and hole-filling in depth images. The denoising is achieved by utilizing a combination of Gaussian kernel filtering and anisotropic filtering. The hole-filling is achieved by utilizing a combination of morphological filtering and zero block filtering. Experimental results using the publicly available datasets are provided indicating the superiority of the developed method in terms of both depth error and computational efficiency compared to three existing methods.

  7. 基于非下采样Contourlet变换和谱图理论的扩散去噪%Image diffusion denoising based on spectral graph theory and nonsubsampled Contourlet transform

    Institute of Scientific and Technical Information of China (English)

    刘国金; 曾孝平; 田逢春; 成可立; 韩亮

    2009-01-01

    提出了一种基于谱图理论的热扩散方程图像去噪方法.该方法用非下采样的Contourlet变换提取图像的边缘和轮廓等几何特征,并将提取的特征用来构造图的权重函数,将扩散方程建立在图上,用热核和拉普拉斯矩阵实现图像的去噪.仿真实验结果表明,该方法能够有效去除高斯噪声,较完整地保持图像中的边缘等细节信息,在去噪性能上优于其他的偏微分方程去噪方法.%A diffusion method based on spectral graph theory for image denoising is proposed, which uses nonsubsampled contourlet transform to capture the geometric feature of the image. After that, a new graph weighting function is constructed based on the captured geometric feature. The heat diffusion equation is generated on a graph. Meantime, a heat kernel and Lapician matrix are used to filter the noisy image. Simulation experiments and comparisons with standard images illustrate the effectiveness of the method. Compared with some other existing methods, the proposed method can effectively reduce Gaussian noise and preserve image edges. Its performance is superior to other partial differential equation methods.

  8. X-Ray image denoising with directional support value transform

    NARCIS (Netherlands)

    Zheng, S.; Hendriks, E.A.; Lei, B.; Hao, W.

    2009-01-01

    Under the support vector machine framework, the support value analysis-based image fusion has been studied, where the salient features of the original images are represented by their support values. The support value transform (SVT)-based image fusion approach have demonstrated some advantages over

  9. 基于四阶偏微分方程的并行图像去噪算法%Parallel Image Denoising Algorithm Based on Fourth-order Partial Differential Equations

    Institute of Scientific and Technical Information of China (English)

    郭茂银; 田有先

    2011-01-01

    四阶偏微分方程(PDE)图像去噪方法具有良好的去噪性能,但该类方法计算量大,耗时长.为提高算法的快速性和有效性,提出一种高效并行的四阶PDE图像去噪算法.该方法基于MPI并行环境,通过分析四阶PDE离散化后差分方程求解的并行性,对噪声图像进行条状重叠的数据划分,采用并行方式对图像去噪,极大地降低了运行时间.%Image noise removal methods based on the fourth-order Partial Differential Equations (PDE) show their good denoising performance, but they also have some problems such as the great amount of calculation and long consuming time. In order to improve the speed and efficiency of the algorithm, an efficient parallel algorithm is proposed in this paper, which can reduce the running time greatly by analyzing the parallelism of the discrete fourth-order PDE and dividing the noise image into overlapping strips in the parallel environment of MPL.

  10. Automated wavelet denoising of photoacoustic signals for burn-depth image reconstruction

    Science.gov (United States)

    Holan, Scott H.; Viator, John A.

    2007-02-01

    Photoacoustic image reconstruction involves dozens or perhaps hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a sample with laser light are used to produce an image of the acoustic source. Each of these point measurements must undergo some signal processing, such as denoising and system deconvolution. In order to efficiently process the numerous signals acquired for photoacoustic imaging, we have developed an automated wavelet algorithm for processing signals generated in a burn injury phantom. We used the discrete wavelet transform to denoise photoacoustic signals generated in an optically turbid phantom containing whole blood. The denoising used universal level independent thresholding, as developed by Donoho and Johnstone. The entire signal processing technique was automated so that no user intervention was needed to reconstruct the images. The signals were backprojected using the automated wavelet processing software and showed reconstruction using denoised signals improved image quality by 21%, using a relative 2-norm difference scheme.

  11. K—SVD算法的超声图像加性噪声去噪研究%The research on denoising of ultrasound image additive noise based on K-SVD Algorithm

    Institute of Scientific and Technical Information of China (English)

    秦晓伟; 郭建中

    2012-01-01

    利用具有稀疏性、特征保持性和可分离性等特点的超完备字典的稀疏表示,基于核奇异值分解(K—SVD)算法,研究了对图像去除噪声效果以及影响效果的因素.理论分析及实验研究表明:K—SVD算法能够很好去除超声图像噪声,保留图像细节特征,获得更高的峰值信噪比(PSNR)值.在计算过程中发现K—SVD算法中的训练样本尺度大小是影响去噪效果的主要参数.%Using the sparse representation of over--completed dictionary which was sparsity, integrity and separability we studied the quality of image denoising and the factors which affected denoising quality in the framework of K-SVD algorithm. Theoretical analysis and experimental study show that the K-SVD algorithm can refrain noise very well, retain more details of the image and obtain better PSNR. The key factor which affects the quality of denoising in K-SVD algorithm is the size of trained pictures.

  12. Group-sparse representation with dictionary learning for medical image denoising and fusion.

    Science.gov (United States)

    Li, Shutao; Yin, Haitao; Fang, Leyuan

    2012-12-01

    Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

  13. Improvement of HMT based on uniform discrete curvelet coeffi-cients and application in image denoising%基于UDCT系数的改进HMT和在图像去噪中应用

    Institute of Scientific and Technical Information of China (English)

    杨兴明; 陈海燕; 王刚; 王彬彬; 赵银平

    2013-01-01

    通过对均匀离散曲波变换(Uniform Discrete Curvelet Transform,UDCT)系数的统计特性研究,同时对系数相关性度量指标互信息量的分析,最终选择隐马尔可夫树模型对其系数建模,且用EM算法训练序列;针对训练时间过长问题,通过分析系数的衰减性和尺度间系数延续性,提出一种新的对算法参数初值的方差和状态转移矩阵的优化方法,实验结果证明,在采用峰值信噪比和相似度作为图像去噪效果的度量时,同等条件下文中提出的算法比Wavelet HMT、Contourlet HMT、UDCT HMT算法有较好的实时性和去噪效果。%Based on the statistical properties of coefficients of the Uniform Discrete Curvelet Transform(UDCT), and the analysis of correlation metric mutual information about the coefficients, this paper chooses the Hidden Markov Tree to model the coefficients finally and trains the sequence with the EM algorithm. With amount of time consuming, an optimization EM algorithm based on HMT of UDCT coefficients is presented; it further optimizes the algorithm by defining the variance and state transition matrix based on the attenuation of coefficients and continuity between the scales. Experimental results show that, in the use of similarity and Peak Signal to Noise Ratio effect as the measurement of image de-noising, under the same conditions, the algorithm proposed has better real-time and de-noising effect than the Wavelet HMT, Contourlet HMT, UDCT HMT algorithm.

  14. Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering

    Institute of Scientific and Technical Information of China (English)

    Zhang Weipeng

    2013-01-01

    In order to preferably identify infrared image of refuge chamber,reduce image noises of refuge chamber and retain more image details,we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising.First,the wavelet transform is adopted to decompose the image of refuge chamber,of which low frequency component remains unchanged.Then,three high-frequency components are treated by bilateral filtering,and the image is reconstructed.The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image,while providing better visual effect.This is superior to using either bilateral filtering or wavelet transform alone.It is useful for perfecting emergency refuge system of coal.

  15. Improved Real-time Denoising Method Based on Lifting Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Liu Zhaohua

    2014-06-01

    Full Text Available Signal denoising can not only enhance the signal to noise ratio (SNR but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

  16. 3-D laser scanning image denoising based on HSSIM and residual ratio threshold%利用 HSSIM 和残差比阈值的3维激光扫描图像去噪

    Institute of Scientific and Technical Information of China (English)

    崔治; 邓曙光; 肖卫初

    2015-01-01

    In order to get better results of 3-D laser scanning image denoising , an improved sparse representation denoising algorithm was proposed by combining histogram structural similarity ( HSSIM ) and residual ratio threshold .The initial over-complete dictionary was applied in the sparse decomposition .The reconstruction error was replaced by similarity factor as fidelity factor.Then the residual ratio threshold was used as the iteration termination of the orthogonal matching pursuit algorithm to reconstruct the denoised image .Finally, the performance data of denoised image , such as peak signal-to-noise ratio(PSNR) and HSSIM, were obtained.The experimental results show that the proposed method could provide better PSNR and HSSIM results compared with the image denoising methods using db 2 wavelet transform , multiscale curve wave transform and discrete cosine transform.Meanwhile , the structural features can be reserved effectively by the proposed method .%为了更好地实现3维激光扫描图像的去噪,提出一种融合直方图结构相似度( HSSIM)和残差比阈值的改进稀疏去噪算法。利用初始化过完备字典进行稀疏分解,以相似因子代替重构误差作为保真项,利用残差比阈值作为正交匹配追踪算法的迭代终止条件对图像进行去噪,获得了去噪后图像的峰值信噪比及HSSIM指标。结果表明,与基于db2小波变换、多尺度曲波变换和离散余弦变换的去噪方法相比,该算法能获得更好的峰值信噪比和HSSIM数据。在有效去除图像噪声的同时还能更有效地保留图像的细节特征。

  17. Image denoising exploiting inter- and intra-scale dependency in complex wavelet domain

    Institute of Scientific and Technical Information of China (English)

    Fengxia Yan; Lizhi Cheng

    2007-01-01

    A new locally adaptive image denoising method, which exploits the intra-scale and inter-scale dependency in the dual-tree complex wavelet domain, is presented. Firstly, a recently emerged bivariate shrinkage rule is extended to a complex coefficient and its neighborhood, the corresponding nonlinear threshold functions are derived from the models using Bayesian estimation theory. Secondly, an adaptive weight, which is able to capture the inter-scale dependency of the complex wavelet coefficients, is combined to the obtained bishrink threshold. The experimental results demonstrate an improved denoising performance over related earlier techniques both in peak signal-to-noise ratio (PSNR) and visual effect.

  18. Partial-differential-equation-based coherence-enhancing denoising for fringe patterns

    Science.gov (United States)

    Wang, Haixia; Qian, Kemao; Gao, Wenjing; Lin, Feng; Seah, Hock Soon

    2008-11-01

    Fringe patterns produced by electronic speckle pattern interferometry (ESPI) are evaluated to measure the deformation on object surfaces. Noise is one of the key problems affecting further processing of the fringe patterns and reduces the final measurement quality. This paper presents a partial differential equations (PDEs) based coherence enhancing denoising model to reduce the noise, enhance the flow-like structure and improve the image quality of fringe patterns. Experimental results show that this filter is flexible and capable of removing most of the noise in ESPI fringe patterns.

  19. Denoising in electronic speckle pattern interferometry fringes by the filtering method based on partial differential equations

    Science.gov (United States)

    Tang, Chen; Zhang, Fang; Yan, Haiqing; Chen, Zhanqing

    2006-04-01

    Denoising in electronic speckle pattern interferometry fringes is the key problem in electronic speckle pattern interferometry. We present the new filtering method based on partial differential equations (called PDE filtering method) to electronic speckle pattern interferometry fringes. The PDE filtering method transforms the image processing to solving the partial differential equations. We test the proposed method on experimentally obtained electronic speckle pattern interferometry fringes, and compare with traditional mean filtering and low-pass Fourier filtering methods. The experimental results show that the technique is capable of effectively removing noise. The PDE filtering method is flexible and has fast computational speed and stable results.

  20. The EM Method in a Probabilistic Wavelet-Based MRI Denoising

    Directory of Open Access Journals (Sweden)

    Marcos Martin-Fernandez

    2015-01-01

    Full Text Available Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak’s, Donoho-Johnstone’s, Awate-Whitaker’s, and nonlocal means filters, in different 2D and 3D images.

  1. Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Othman, Ahmed E. [RWTH Aachen University, Department of Diagnostic and Interventional Neuroradiology, Aachen (Germany); Eberhard Karls University Tuebingen, University Hospital Tuebingen, Department for Diagnostic and Interventional Radiology, Tuebingen (Germany); Brockmann, Carolin; Afat, Saif; Pjontek, Rastislav; Nikoubashman, Omid; Brockmann, Marc A.; Wiesmann, Martin [RWTH Aachen University, Department of Diagnostic and Interventional Neuroradiology, Aachen (Germany); Yang, Zepa; Kim, Changwon [Seoul National University, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Suwon (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Nikolaou, Konstantin [Eberhard Karls University Tuebingen, University Hospital Tuebingen, Department for Diagnostic and Interventional Radiology, Tuebingen (Germany); Kim, Jong Hyo [Seoul National University, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Suwon (Korea, Republic of); Seoul National University College of Medicine, Department of Radiology, Seoul (Korea, Republic of); Advanced Institute of Convergence Technology, Center for Medical-IT Convergence Technology Research, Suwon (Korea, Republic of); Seoul National University Hospital, Department of Radiology, Seoul (Korea, Republic of)

    2016-01-15

    To examine the impact of denoising on ultra-low-dose volume perfusion CT (ULD-VPCT) imaging in acute stroke. Simulated ULD-VPCT data sets at 20 % dose rate were generated from perfusion data sets of 20 patients with suspected ischemic stroke acquired at 80 kVp/180 mAs. Four data sets were generated from each ULD-VPCT data set: not-denoised (ND); denoised using spatiotemporal filter (D1); denoised using quanta-stream diffusion technique (D2); combination of both methods (D1 + D2). Signal-to-noise ratio (SNR) was measured in the resulting 100 data sets. Image quality, presence/absence of ischemic lesions, CBV and CBF scores according to a modified ASPECTS score were assessed by two blinded readers. SNR and qualitative scores were highest for D1 + D2 and lowest for ND (all p ≤ 0.001). In 25 % of the patients, ND maps were not assessable and therefore excluded from further analyses. Compared to original data sets, in D2 and D1 + D2, readers correctly identified all patients with ischemic lesions (sensitivity 1.0, kappa 1.0). Lesion size was most accurately estimated for D1 + D2 with a sensitivity of 1.0 (CBV) and 0.94 (CBF) and an inter-rater agreement of 1.0 and 0.92, respectively. An appropriate combination of denoising techniques applied in ULD-VPCT produces diagnostically sufficient perfusion maps at substantially reduced dose rates as low as 20 % of the normal scan. (orig.)

  2. Voxel-Wise Functional Connectomics Using Arterial Spin Labeling Functional Magnetic Resonance Imaging: The Role of Denoising.

    Science.gov (United States)

    Liang, Xiaoyun; Connelly, Alan; Calamante, Fernando

    2015-11-01

    The objective of this study was to investigate voxel-wise functional connectomics using arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI). Since ASL signal has an intrinsically low signal-to-noise ratio (SNR), the role of denoising is evaluated; in particular, a novel denoising method, dual-tree complex wavelet transform (DT-CWT) combined with the nonlocal means (NLM) algorithm is implemented and evaluated. Simulations were conducted to evaluate the performance of the proposed method in denoising images and in detecting functional networks from noisy data (including the accuracy and sensitivity of detection). In addition, denoising was applied to in vivo ASL datasets, followed by network analysis using graph theoretical approaches. Efficiencies cost was used to evaluate the performance of denoising in detecting functional networks from in vivo ASL fMRI data. Simulations showed that denoising is effective in detecting voxel-wise functional networks from low SNR data and/or from data with small total number of time points. The capability of denoised voxel-wise functional connectivity analysis was also demonstrated with in vivo data. We concluded that denoising is important for voxel-wise functional connectivity using ASL fMRI and that the proposed DT-CWT-NLM method should be a useful ASL preprocessing step.

  3. Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

    Directory of Open Access Journals (Sweden)

    Vijay G. S.

    2012-01-01

    Full Text Available The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR and reducing the root-mean-square error (RMSE. In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN and the Support Vector Machine (SVM, for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC. Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.

  4. 一种基于小波变换的偏微分方程图像去噪方法%Image denoising method of partial differential equation based on wavelet transform

    Institute of Scientific and Technical Information of China (English)

    张力娜; 李小林

    2013-01-01

    The texture information is easy to be polished when denoising the images,especially the linear structure of the texture is easy to be damaged.A method is put forward that smooth sub-image by using modified method of combination of P-M diffusion and coherence enhancing diffusion in wavelet domain,the different wavelet sub-bands are diffused with the improved method and reconstructed,then the denoising image is obtained.Numerical experiments show that the proposed method have better effect on texture image denoising,especially on keeping the texture information,the linear structure and smoothness of the texture.%针对纹理图像在去除噪声时,纹理信息容易被磨光,尤其是纹理的线状结构很容易被破坏的问题,提出在小波域改进耦合P-M扩散与相干增强扩散的方法,并用改进的方法对不同的小波子带进行扩散,然后重构,得到去噪图像.数值实验结果表明,本文方法在达到一定降噪效果,保持区域内部较好光滑性的同时,对保持纹理信息、纹理的线状结构及纹理的光滑有很好的效果,说明该方法对纹理图像去噪有较好的效果.

  5. Denoising of hyperspectral images by best multilinear rank approximation of a tensor

    Science.gov (United States)

    Marin-McGee, Maider; Velez-Reyes, Miguel

    2010-04-01

    The hyperspectral image cube can be modeled as a three dimensional array. Tensors and the tools of multilinear algebra provide a natural framework to deal with this type of mathematical object. Singular value decomposition (SVD) and its variants have been used by the HSI community for denoising of hyperspectral imagery. Denoising of HSI using SVD is achieved by finding a low rank approximation of a matrix representation of the hyperspectral image cube. This paper investigates similar concepts in hyperspectral denoising by using a low multilinear rank approximation the given HSI tensor representation. The Best Multilinear Rank Approximation (BMRA) of a given tensor A is to find a lower multilinear rank tensor B that is as close as possible to A in the Frobenius norm. Different numerical methods to compute the BMRA using Alternating Least Square (ALS) method and Newton's Methods over product of Grassmann manifolds are presented. The effect of the multilinear rank, the numerical method used to compute the BMRA, and different parameter choices in those methods are studied. Results show that comparable results are achievable with both ALS and Newton type methods. Also, classification results using the filtered tensor are better than those obtained either with denoising using SVD or MNF.

  6. 一种基于自适应阈值的图像去噪新方法%Adaptive Wavelet Thresholding for Image Denoising

    Institute of Scientific and Technical Information of China (English)

    尚晓清; 王军锋; 宋国乡

    2003-01-01

    Selecting threshold is the most important in threshold-based nonlinear filtering by wavelet transform. In this paper, a novel adaptive threshold is proposed by minimizing a Bayesian risk(It is adaptive to subband because it depends on data-driven estimates of the parameters). Combining this thresholding method with Wiener fitting can re-sult a new denoising method. Expermental results show that the proposed method indeed remove noise significantly and retaining most image edges. The results compare favorably with the reported results in the recent denoising liter-ature.

  7. SET OPERATOR-BASED METHOD OF DENOISING MEDICAL VOLUME DATA

    Institute of Scientific and Technical Information of China (English)

    程兵; 郑南宁; 袁泽剑

    2002-01-01

    Objective To investigate impulsive noise suppression of medical volume data. Methods The volume data is represented as level sets and a special set operator is defined and applied to filtering it. The small connected components, which are likely to be produced by impulsive noise, are eliminated after the filtering process. A fast algorithm that uses a heap data structure is also designed. Results Compared with traditional linear filters such as a Gaussian filter, this method preserves the fine structure features of the medical volume data while removing noise, and the fast algorithm developed by us reduces memory consumption and improves computing efficiency. The experimental results given illustrate the efficiency of the method and the fast algorithm. Conclusion The set operator-based method shows outstanding denoising properties in our experiment, especially for impulsive noise. The method has a wide variety of applications in the areas of volume visualization and high dimensional data processing.

  8. 基于多小波分析的图像优化去噪方法研究%Image Optimization De-noising Method Research Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    何永峰

    2015-01-01

    针对传统单小波在对称性、正交性、有限支撑等特性上的不足,提出一种基于多小波分析的图像优化去噪方法.对图像进行预滤波处理操作,消除多小波的不恰当离散性.对待处理信号经预滤波处理后产生的四个分量进行多小波变换处理.采用Visu shrink方法与基于stein无风险估计的Sure shrink方法对阈值进行确定.保持总像素量不变,给出多小波分解及重构系统框图.以S=2的多小波为例对多小波变换进行分析.分别完成行滤波和列滤波.对噪声方差进行预测,完成对原始图像多小波系数的方差的估计,对尺度参数和阈值进行计算.仿真实验结果表明,所提方法能够有效去除图像噪声,进一步增强了图像的信噪比.%In view of the traditional unit-wavelet in symmetry, orthogonality, limited support features such as the inadequa-cy that go up, put forward a kind of based on wavelet analysis of image denoising method optimization.Pre filter on image processing operations, eliminating inappropriate discrete multiwavelet.Dealing with the signal after pre filtering treatment on the four components of multiwavelet transform processing.Using Visu shrink method and based on Sure stein, risk-free estimated the shrink method to determine the threshold.The total amount of pixels unchanged, wavelet decomposition and reconstruction system block diagram is given.Based on the wavelet S= 2, for example analysis of multiwavelet transform. Complete line filtering and column filtering respectively.Forecast the noise variance, the completion of the original image wavelet coefficients more the variances of the estimates, the dimension parameters and the threshold value is calculated. The simulation results show that the proposed method can effectively remove the noise of the image, further enhance the SNR of the image.

  9. Two Level DCT and Wavelet Packets Denoising Robust Image Watermarking

    Directory of Open Access Journals (Sweden)

    N.Koteswara Rao

    2014-01-01

    Full Text Available In this paper we present a low frequency watermarking scheme on gray level images, which is based on DCT transform and spread spectrum communications technique.The DCT of non overlapping 8x8 blocks of the host image is computed, then using each block DC coefficients we construct a low-resolution approximation image. We apply block based DCT on this approximation image, then a pseudo random noise sequence is added into its high frequencies. For detection, we extract the approximation image from the watermarked image, then the same pseudo random noise sequence is generated, and its correlation is computed with high frequencies of the watermarked approximation image. In our method, higher robustness is obtained because of embedding the watermark in low frequency. In addition, higher imperceptibility is gained by scattering the watermark's bit in different blocks. We evaluated the robustness of the proposed technique against many common attacks such as JPEG compression, additive Gaussian noise and median filter. Compared with related works, our method proved to be highly resistant in cases of compression and additive noise, while preserving high PSNR for the watermarked images.

  10. System and method for image reconstruction, analysis, and/or de-noising

    KAUST Repository

    Laleg-Kirati, Taous-Meriem

    2015-11-12

    A method and system can analyze, reconstruct, and/or denoise an image. The method and system can include interpreting a signal as a potential of a Schrödinger operator, decomposing the signal into squared eigenfunctions, reducing a design parameter of the Schrödinger operator, analyzing discrete spectra of the Schrödinger operator and combining the analysis of the discrete spectra to construct the image.

  11. Image denoising based on adaptive graph regularization%图上自适应正则化的图像去噪

    Institute of Scientific and Technical Information of China (English)

    刘国金; 曾孝平; 刘刈

    2012-01-01

    Adaptive regularization can select different parameters based on the features of local areas in an image, which can differentiate the edges and noise in an image flexibly. An adaptive graph regularization is proposed based on graph spectral theory and adaptive regularization, which uses the Non local means to generate the weighting function of graph. The adaptive graph regularization equation is used to filter the noisy image. Simulation results show that the proposed method can effectively remove the noise and is superior to other graph theory based partial differential equation methods.%自适应正则化方法在不同的局部区域能够选取不同的正则化参数和正则化约束,因而能够灵活地对边缘和噪声进行区别处理。将自适应正则化建立在图上,提出了一种定义在加权图上的,具有自适应参数的正则化模型。用nonlocal means算法构造图的权重函数,用建立在图上的自适应正则化方程实现图像的去噪处理,仿真实验结果表明:该方法能有效地去除图像中的噪声,在去噪性能上优于部分基于图论的偏微分方程方法。

  12. Research of Signal De-noising Technique Based on Wavelet

    Directory of Open Access Journals (Sweden)

    Shigang Hu

    2013-09-01

    Full Text Available During the process of signal testing, often exposed to interference and influence of all kinds of noise signal, such as data collection and transmission and so may introduce noise. So in practical applications, before analysis of the data measured, the need for de-noising processing. The signal de-noising is a method for filtering the high frequency  noise of the signal and makes the signal more precise. This paper deals with the general theory of wavelet transform, the application of wavelet transform in signal de-noising as well as the analysis of the characteristics of noise-polluted signa1. Matlab is used to be carried out the simu1ation where the different wavelet and different threshold of the same wavelet for signal de-noising are applied. An indicator of wavelet de-noising is presented , it is the indicator of smoothness. Through analysis of the experiment , considered MSE , SNR and smoothness , it can be a good way to evaluate the equality of wavelet de-noising. The results show that the wavelet transform can achieve excellent results in signal de-noising.  

  13. Color Image Denoising Using Stationary Wavelet Transform and Adaptive Wiener Filter

    Directory of Open Access Journals (Sweden)

    Iman M.G. Alwan

    2012-01-01

    Full Text Available The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing. Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by using MATLAB R2010a with color images contaminated by white Gaussian noise. Compared with stationary wavelet and wiener filter algorithms, the experimental results show that the proposed method provides better subjective and objective quality, and obtain up to 3.5 dB PSNR improvement.

  14. Adaptive nonlocal means filtering based on local noise level for CT denoising

    Energy Technology Data Exchange (ETDEWEB)

    Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando, E-mail: manduca.armando@mayo.edu [Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905 (United States); Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H. [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)

    2014-01-15

    Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the

  15. CONVEF-based Fourth-order Anisotropic Diffusion for Image Denoising%基于CONVEF的四阶各向异性扩散及图像去噪

    Institute of Scientific and Technical Information of China (English)

    王元全; 任文琦

    2013-01-01

    偏微分方程在图像去噪中有广泛的应用.传统的二阶偏微分方程虽然具有较好的去噪效果,但是处理得到的结果容易产生阶梯效应,这种现象会引起后续图像处理的误判断.You和Kaveh提出了四阶偏微分方程,该模型可以有效的去除阶梯效应,但由于该算法是一个各向同性的滤波算法,因此图像的边缘保护能力有所降低,使去噪结果中边缘和纹理等细节信息丢失.针对以上缺点,提出了基于卷积虚拟电子场(CONVEF)的四阶偏微分方程.新的模型降低了图像在边缘方向的扩散,得到一个有效的各向异性扩散模型,从而在去噪的同时可以更好的保护图像的边缘、纹理等细节特征.%Partial differential equations (PDEs) have been justified as effective tools for image denoising.The second-order PDEs are effective for image noise removal but they can lead to staircase effects.These staircases can be falsely detected as edges in the successive image processing.The fourth-order PDE introduced by You and Kaveh can alleviate the staircase effect,but it is an isotropic filter and its edge and texture preserving ability is not satisfactory.In light of this,the convolutional virtual electric field (CONVEF) into the fourth-order PDE for images restoration is introduced.Since the CONVEF based fourth-order model possesses anisotropic properties over the image features,it leads to improvement on noise removal and edge and texture preserving over the original model.

  16. Image Denoising Using Total Variation Model Guided by Steerable Filter

    Directory of Open Access Journals (Sweden)

    Wenxue Zhang

    2014-01-01

    Full Text Available We propose an adaptive total variation (TV model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.

  17. Algorithm of Image Denoising Based on High Order Partial Differential Equation%高斯平滑算子对Y -K 模型改进的图像去噪方法

    Institute of Scientific and Technical Information of China (English)

    叶衍昌; 赵东红; 李小娟

    2013-01-01

    研究了在偏微分方程理论框架下进行图像去噪的方法,重点对二阶和四阶偏微分方程的主要去噪方法进行了分析。二阶偏微方程模型中的 P -M 模型能很好地去除噪声,但时常会出现块状现象;四阶偏微分方程模型中Y -K模型能消除阶梯效应但会出现斑点现象。使用Gilboa扩散系数、中值滤波器和高斯平滑算子对Y -K模型进行改进。实验证明,新模型减少了迭代次数,提高了算法效率,也一定程度上避免了块状及斑点现象。%This paper studied the theoretical framework of the partial differential equations for image denoising method , the second and fourth order partial differential equations of the main denoising method were discussed and a new algo-rithm .The P-M model of the second order partial differential equations denoised well ,but them led to a"massive phenom-enon ".The Y-K model of the fourth order partial differential equations eliminate the step effect but will appear the speck-le phenomenon .A new model introduced is proposed that the Y-K model is improved by using The diffusion coefficient of Gilboa ,median filter and Gaussian smoothing operator .A large extent ,the new model proved to reduce the number of it-erations ,but also to some extent to avoid a "massive phenomenon "and the speckle .

  18. Image Denoising Based on Improved K-SVD and Non-local Regularization%基于改进K-SVD和非局部正则化的图像去噪

    Institute of Scientific and Technical Information of China (English)

    杨爱萍; 田玉针; 何宇清; 董翠翠

    2015-01-01

    In view of the poor performance of the K-Singular Value Decomposition( K-SVD) denoising method,a new algorithm is proposed. The denoising performance is improved by the refined K-SVD method with the help of the correlation coefficient matching criterion and dictionary cutting method. By combining the non-local self-similarity as a constrained regularization into the image denoising model,the performance is further enhanced. Experimental results show that compared with traditional K-SVD method, this algorithm can effectively improve the smoothness of homogeneous regions with preserving more texture and edge details.%K-奇异值分解( K-SVD)算法在强噪声下的去噪性能较差。为此,提出一种新的图像去噪算法。使用相关系数匹配准则和噪声原子裁剪方法改进传统K-SVD算法,提高原算法的去噪性能,将非局部正则项融入图像去噪模型,并采用非局部自相似性进一步改善图像的去噪效果。实验结果表明,与传统K-SVD算法相比,该算法在提高同质区域平滑性的同时,能保留更多的纹理、边缘等细节特征。

  19. 基于自由分布式FDR阈值的NSCT遥感图像去噪%NSCT Remote Sensing Image Denoising Based on Free Distributed FDR Threshold

    Institute of Scientific and Technical Information of China (English)

    刘继红

    2014-01-01

    提出一种新的自由分布式(FDR)假设检验阈值和非下采样Contourlet变换(NSCT)相结合的图像去噪方法。该方法首先在NSCT域利用统计学中的自由分布式错误发现率(FDR)假设检验方法来设定阈值,再通过软阈值函数进行去噪,且不依赖信号的长度。实验结果表明:该方法能够更有效地去除。感图像的高斯噪声,提高图像的峰值信噪比;同时利用NSCT变换的平移不变性抑制了去噪中的伪Gibbs失真效应,较完整地保持了图像的纹理和边缘等细节信息,从而明显改善了图像的视觉效果。%A new method for image denoising based on the free distributed hypothesis test threshold ( FDR ) and the non-sub-sampled contourlet transform ( NSCT ) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn't depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remote sensing image; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details' information of the image, thus obviously ameliorate the visual effect of the image .

  20. DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

    Directory of Open Access Journals (Sweden)

    Purushotham Arnie

    2011-10-01

    Full Text Available Abstract Background Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression with independent and highly curated structural interaction data (e.g protein interaction networks or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study. Results We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer. Conclusions Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.

  1. The Remote Sensing Image Denoising of“The First Satellite of High Resolution”Based on Sparse Representation and Dictionary Learning%基于稀疏表示和自适应字典学习的“高分一号”遥感图像去噪

    Institute of Scientific and Technical Information of China (English)

    秦振涛; 杨武年; 潘佩芬

    2013-01-01

    Denoising the high resolution remote sensing images is a difficult problem in the relative research field of remote sensing. A novel algorithm for denoising the high resolution remote sensing images is proposed based on sparse representation. A dictionary which has an efficient description of remote sensing image content is obtained based on K-SVD algorithm according to the characteristics of the added noise of high spatial resolution remote sensing images. Denoising is realized by using sparse representation, and the useful information of the image is kept. The experimental results of the remote sensing images obtained by“the first satellite of high resolution”show that the algorithm can filter out the noise in the image more effectively and improve the PSNR, and this method has better performance than other dictionary learning algorithms and other denoising algorithms.%对高分辨率遥感图像进行去噪是遥感研究中的一个重要难题。本文提出了一种新的基于稀疏表示的高分辨率遥感图像去噪算法,该算法根据加噪高分辨率遥感图像的特点利用 K-SVD 算法自适应的学习得到能高效描述遥感图像内容的字典,利用稀疏表示实现去噪,并且保留原图像的有用信息。通过对“高分一号”获取的遥感图像进行实验表明,该算法能较好地滤除遥感图像的噪声,提高了图像的峰值信噪比,该方法比其他字典学习算法及其他去噪算法具有更好的性能。

  2. Denoising method of heart sound signals based on self-construct heart sound wavelet

    Science.gov (United States)

    Cheng, Xiefeng; Zhang, Zheng

    2014-08-01

    In the field of heart sound signal denoising, the wavelet transform has become one of the most effective measures. The selective wavelet basis is based on the well-known orthogonal db series or biorthogonal bior series wavelet. In this paper we present a self-construct wavelet basis which is suitable for the heart sound denoising and analyze its constructor method and features in detail according to the characteristics of heart sound and evaluation criterion of signal denoising. The experimental results show that the heart sound wavelet can effectively filter out the noise of the heart sound signals, reserve the main characteristics of the signal. Compared with the traditional wavelets, it has a higher signal-to-noise ratio, lower mean square error and better denoising effect.

  3. Denoising method of heart sound signals based on self-construct heart sound wavelet

    Directory of Open Access Journals (Sweden)

    Xiefeng Cheng

    2014-08-01

    Full Text Available In the field of heart sound signal denoising, the wavelet transform has become one of the most effective measures. The selective wavelet basis is based on the well-known orthogonal db series or biorthogonal bior series wavelet. In this paper we present a self-construct wavelet basis which is suitable for the heart sound denoising and analyze its constructor method and features in detail according to the characteristics of heart sound and evaluation criterion of signal denoising. The experimental results show that the heart sound wavelet can effectively filter out the noise of the heart sound signals, reserve the main characteristics of the signal. Compared with the traditional wavelets, it has a higher signal-to-noise ratio, lower mean square error and better denoising effect.

  4. A Comparative Study between X_Lets Family for Image Denoising

    Directory of Open Access Journals (Sweden)

    Beladgham Mohamed

    2014-02-01

    Full Text Available Research good representation is a problem in image processing for this, our works are focused in developing and proposes some new transform which can represent the edge of image more efficiently, Among these transform we find the wavelet and ridgelet transform these both types transforms are not optimal for images with complex geometry, so we replace this two types classical transform with other effectiveness transform named bandelet transform, this transform is appropriate for the analysis of edges of the images and can preserve the detail information of high frequency of noisy image. De-noising is one of the most interesting and widely investigated topics in image processing area. In order to eliminate noise we exploit in this paper the geometrical advantages offered by the bandelet transform to solve the problem of image de-noising. To arrive to determine which type transform allows us high quality visual image, a comparison is made between bandelet, curvelet, ridgelet and wavelet transform, after determining the best transform, we going to determine which type of image is adapted to this transform. Numerically, we show that bandelet transform can significantly outperform and gives good performances for medical image type TOREX, and this is justified by a higher PSNR value for gray images.

  5. BL_Wiener Denoising Method for Removal of Speckle Noise in Ultrasound Image

    Directory of Open Access Journals (Sweden)

    Suhaila Sari

    2015-02-01

    Full Text Available Medical imaging techniques are extremely important tools in medical diagnosis. One of these important imaging techniques is ultrasound imaging. However, during ultrasound image acquisition process, the quality of image can be degraded due to corruption by speckle noise. The enhancement of ultrasound images quality from the 2D ultrasound imaging machines is expected to provide medical practitioners more reliable medical images in their patients’ diagnosis. However, developing a denoising technique which could remove noise effectively without eliminating the image’s edges and details is still an ongoing issue. The objective of this paper is to develop a new method that is capable to remove speckle noise from the ultrasound image effectively. Therefore, in this paper we proposed the utilization of Bilateral Filter and Adaptive Wiener Filter (BL_Wiener denoising method for images corrupted by speckle noise. Bilateral Filter is a non-linear filter effective in removing noise, while Adaptive Wiener Filter balances the tradeoff between inverse filtering and noise smoothing by removing additive noise while inverting blurring. From our simulation results, it is found that the BL_Wiener method has improved between 0.89 [dB] to 3.35 [dB] in terms of PSNR for test images in different noise levels in comparison to conventional methods.

  6. Noise analyzing and denoising of intensity image for laser active imaging system%激光主动成像图像噪声分析与抑制

    Institute of Scientific and Technical Information of China (English)

    李晓峰; 徐军; 罗积军; 曹立佳; 张胜修

    2011-01-01

    The noise mechanism of laser active imaging system was analyzed.In order to suppress the noise of intensity image, a new image denoising algorithm for laser active imaging system which based on homomorphic filtering and wavelet domain Stein's Unbiased Risk Estimate (SURE) was proposed.First, it made the speckle noise convert to the additive noise by homomorphic transform, and then minimized the estimate of mean square error between the clean image and the denoised one in wavelet domain.Contrary to the custom methods, wavelet coefficients were not considered as random variable anymore, the denoising process were parameterized directly as a sum of elementary nonlinear processes with unknown weights.The SURE was introduced to obtain the near-optimal weights.Finally, inverse wavelet transform and inverse homomorpgic transform were carried out and the denoised intensity image was obtained.Experimental results show that the proposed algorithm has advanced denoising performance and the computation time is less than others.%分析了激光主动成像图像的噪声机理,针对强度像噪声抑制问题,提出了一种基于同态滤波和小波域SURE(Stein's Unbiased Risk Estimate)的激光主动成像图像降噪算法.该方法首先用同态变换将乘性散斑噪声转换为加性噪声;然后在小波域,没有将小波系数看作随机变量.而是以最小化均方误差MSE为目的,将图像降噪过程看作是一个小波系数的加权和.通过SURE获得近似最优的小波系数的权值;最后再作相应的小波逆变换和同态逆变换,得到降噪后的图像.实验结果表明:该方法具有较好的噪声抑制效果,且计算量极小.

  7. 梯度加权的高阶变分图像去噪模型��%High Order Variational Image Denoising Model with Gradient Based Weight

    Institute of Scientific and Technical Information of China (English)

    芦碧波; 王建龙; 王静

    2015-01-01

    针对现有高阶变分模型不能很好保持边界的问题,引入卷积后的一阶梯度信息作为二阶导数的加权函数,建立了一个新的高阶变分能量泛函,并得到了四阶偏微分方程扩散模型。在加权系数的构造中,在分析经典二阶全变分扩散模型结构的基础上,给出了具有一定边缘保持能力的加权函数设计方案。此加权函数可判断图像局部区域结构,自适应调整扩散速度,有利于在扩散中保留细节。数值实验表明,该模型可以有效去除噪声,消除阶梯效应,避免边界振荡,具有较好的边界保持性质。%To improve the edge preserving ability of current high order models, this paper proposes a new high order variational energy function by introducing the gradient information after convolution as the weighting function of second derivative, and leads to a fourth-order partial differential equation diffusion model. In the procedure of con-structing the weighting coefficients, this paper gives a scheme that makes weighting function have the ability to pre-serve a certain edge based on the analysis of classical second-order total variation diffusion model. This weighting function can determine local structure region of the image and adapt the diffusion rate, as well as avoid boundary oscil-lations with good retention properties of the detail. The experimental results show that the proposed model can relief staircase effect, avoid oscillations and preserve edges while removing noise.

  8. 基于变分法和剪切波耦合算法的蝗虫切片保纹理图像降噪%Denoising for locust slice image with texture preserving based on coupling technology of variational method and shearlet transform

    Institute of Scientific and Technical Information of China (English)

    梅树立

    2016-01-01

    in the images as the smooth domains, this results in detail texture in the images being often destroyed. To solve the problem, we proposed a coupling technology of the multi-scale variational method based on the interpolation wavelet frame and the shearlet transform, in which the variational method was employed to identify the contour and the shearlet transform to describe the texture precisely. According to this method, the image was firstly decomposed and reconstructed by means of the shearlet transform, which can remove most of the noise in the image. Second, the variational method based on the multi-scale interpolative wavelet frame was employed to smooth the denoised image. This can divide the image into some domains, and they possessed different texture feature which could be identified by means of the correlation value derived from the gray-level co-occurrence matrix of the grayscale image. Compared to the variational method under the total variation (TV) frame, the multi-scale interpolative wavelet frame can identify more detail domains which was helpful to improve the quality of the denoised images. To the image noised by the Gauss noise with the standard derivation 10, the peak signal to noise ratio (PSNR) of the denoised image obtained by the variational method based on the multi-scale interpolative wavelet frame was 1.3131 larger than PSRN obtained by one based on the TV frame, and the structural similarity image measurement (SSIM) increased by 4.5% accordingly. Next, the variational method and the shearlet transform method can be used to remove the noises existed in the cartoon region and the texture region, respectively. This can overcome the shortcomings of only one method and remain all the merits in all the denoising algorithms. For instance, median filtering can be applied to remove the noise in the smooth domain, and the shearlet transform can be employed to remove the noise in the domain with abundant textures. Last, the locust slice images were

  9. Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function

    Science.gov (United States)

    He, Yunlong; Zhao, Yanna; Ren, Yanju; Gee, James

    2017-01-01

    Filtering belongs to the most fundamental operations of retinal image processing and for which the value of the filtered image at a given location is a function of the values in a local window centered at this location. However, preserving thin retinal vessels during the filtering process is challenging due to vessels' small area and weak contrast compared to background, caused by the limited resolution of imaging and less blood flow in the vessel. In this paper, we present a novel retinal image denoising approach which is able to preserve the details of retinal vessels while effectively eliminating image noise. Specifically, our approach is carried out by determining an optimal spatial kernel for the bilateral filter, which is represented by a line spread function with an orientation and scale adjusted adaptively to the local vessel structure. Moreover, this approach can also be served as a preprocessing tool for improving the accuracy of the vessel detection technique. Experimental results show the superiority of our approach over state-of-the-art image denoising techniques such as the bilateral filter. PMID:28261320

  10. Application of the discrete torus wavelet transform to the denoising of magnetic resonance images of uterine and ovarian masses

    Science.gov (United States)

    Sarty, Gordon E.; Atkins, M. Stella; Olatunbosun, Femi; Chizen, Donna; Loewy, John; Kendall, Edward J.; Pierson, Roger A.

    1999-10-01

    A new numerical wavelet transform, the discrete torus wavelet transform, is described and an application is given to the denoising of abdominal magnetic resonance imaging (MRI) data. The discrete tori wavelet transform is an undecimated wavelet transform which is computed using a discrete Fourier transform and multiplication instead of by direct convolution in the image domain. This approach leads to a decomposition of the image onto frames in the space of square summable functions on the discrete torus, l2(T2). The new transform was compared to the traditional decimated wavelet transform in its ability to denoise MRI data. By using denoised images as the basis for the computation of a nuclear magnetic resonance spin-spin relaxation-time map through least squares curve fitting, an error map was generated that was used to assess the performance of the denoising algorithms. The discrete torus wavelet transform outperformed the traditional wavelet transform in 88% of the T2 error map denoising tests with phantoms and gynecologic MRI images.

  11. Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

    Science.gov (United States)

    Duan, Yabo; Song, Chengtian

    2016-12-01

    Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.

  12. Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

    Science.gov (United States)

    Duan, Yabo; Song, Chengtian

    2016-10-01

    Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.

  13. Non-Local Means Denoising

    Directory of Open Access Journals (Sweden)

    Antoni Buades

    2011-09-01

    Full Text Available We present in this paper a new denoising method called non-local means. The method is based on a simple principle: replacing the color of a pixel with an average of the colors of similar pixels. But the most similar pixels to a given pixel have no reason to be close at all. It is therefore licit to scan a vast portion of the image in search of all the pixels that really resemble the pixel one wants to denoise. The paper presents two implementations of the method and displays some results.

  14. Static Myocardial Perfusion Imaging using denoised dynamic Rb-82 PET/CT scans

    DEFF Research Database (Denmark)

    Petersen, Maiken N.M.; Hoff, Camilla; Harms, Hans

    to the hottest pixel) was compared in order to assess whether a correlation between original image data and denoised image data could be found. In addition to this, correlations for TPD, Extent of defect and summed defect scores (SSS, SRS and SDS) were investigated. The data was analysed using linear regression......Introduction: Relative and absolute measures of myocardial perfusion are derived from a single 82Rb PET/CT scan. However, images are inherently noising due to the short half-life of 82Rb. We have previously shown that denoising techniques can be applied to dynamic 82Rb series with excellent...... and Bland-Altman analysis. Results: For HYPR-LR, a good correlation was found for relative segmental perfusion for both stress (y=1.007x+0.313, R2=0.98) and rest (y=1.007x+ 0.421, R2=0.96) scans with negative bias of -0.79±1.44 and -0.90±1.63, respectively. Correlations for SSS (R2=0.94), SRS (R2=0.92), SDS...

  15. Medical image denoising via optimal implementation of non-local means on hybrid parallel architecture.

    Science.gov (United States)

    Nguyen, Tuan-Anh; Nakib, Amir; Nguyen, Huy-Nam

    2016-06-01

    The Non-local means denoising filter has been established as gold standard for image denoising problem in general and particularly in medical imaging due to its efficiency. However, its computation time limited its applications in real world application, especially in medical imaging. In this paper, a distributed version on parallel hybrid architecture is proposed to solve the computation time problem and a new method to compute the filters' coefficients is also proposed, where we focused on the implementation and the enhancement of filters' parameters via taking the neighborhood of the current voxel more accurately into account. In terms of implementation, our key contribution consists in reducing the number of shared memory accesses. The different tests of the proposed method were performed on the brain-web database for different levels of noise. Performances and the sensitivity were quantified in terms of speedup, peak signal to noise ratio, execution time, the number of floating point operations. The obtained results demonstrate the efficiency of the proposed method. Moreover, the implementation is compared to that of other techniques, recently published in the literature.

  16. A NEW DE-NOISING METHOD BASED ON 3-BAND WAVELET AND NONPARAMETRIC ADAPTIVE ESTIMATION

    Institute of Scientific and Technical Information of China (English)

    Li Li; Peng Yuhua; Yang Mingqiang; Xue Peijun

    2007-01-01

    Wavelet de-noising has been well known as an important method of signal de-noising.Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied widely.Compared with traditional 2-band wavelet,3-band wavelet has advantages in many aspects.According to this theory,an adaptive signal de-noising method in 3-band wavelet domain based on nonparametric adaptive estimation is proposed.The experimental results show that in 3-band wavelet domain,the proposed method represents better characteristics than Donoho method in protecting detail and improving the signal-to-noise ratio of reconstruction signal.

  17. An Image Denoising Framework with Multi-resolution Bilateral Filtering and Normal Shrink Approach

    Directory of Open Access Journals (Sweden)

    Shivani Sharma

    2014-02-01

    Full Text Available In this study, an image denoising algorithm is presented, which takes into account wavelet thresholding and bilateral filtering in transform domain. The proposed algorithm gives an extension of the bilateral filter i.e., multiresolution bilateral filter, in which bilateral filtering is applied to the approximation sub bands and normal shrink is used for thresholding the wavelet coefficients of the detail sub bands of an image decomposed using a wavelet filter bank up to 2-level of decomposition. The algorithm is tested against ultrasound image of gall bladder corrupted by different types of noise namely, gaussian, speckle, poisson and impulse. The result shows that with increase in decomposition levels the proposed method is effective in eliminating noise but gives overly smoothed image. The algorithm outperforms with speckle and poisson noise at 2- level decomposition in terms of PSNR.

  18. Video Denoising based on Stationary Wavelet Transform and Center Weighted Median Filter

    Directory of Open Access Journals (Sweden)

    Soundarya K

    2014-01-01

    Full Text Available Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. Images are getting corrupted by impulse noise during image acquisition and transmission. A new median filter termed as the center weighted median filter (CWMF in the wavelet coefficient domain combined with stationary wavelet transform (SWT is proposed for video denoising. This filter iteratively smoothes the noisy wavelet coefficients variances preserving the edge information contained in the large magnitude wavelet coefficients. This Paper deals with uncompressed video of .avi format. The proposed algorithm works well for suppressing Gaussian noise with noise density from 10 to 70% while preserving image details. Simulation results show that higher peak signal to noise ratio can be obtained as compared to other recent image denoising methods.

  19. Image denoising using a directional adaptive diffusion filter

    Science.gov (United States)

    Zhao, Cuifang; Shi, Caicheng; He, Peikun

    2006-11-01

    Partial differential equations (PDEs) are well-known due to their good processing results which it can not only smooth the noise but also preserve the edges. But the shortcomings of these processes came to being noticed by people. In some sense, PDE filter is called "cartoon model" as it produces an approximation of the input image, use the same diffusion model and parameters to process noise and signal because it can not differentiate them, therefore, the image is naturally modified toward piecewise constant functions. A new method called a directional adaptive diffusion filter is proposed in the paper, which combines PDE mode with wavelet transform. The undecimated discrete wavelet transform (UDWT) is carried out to get different frequency bands which have obviously directional selectivity and more redundancy details. Experimental results show that the proposed method provides a performance better to preserve textures, small details and global information.

  20. 基于四阶PDEs和对比度增强的荧光显微图像去噪%Fluorescence Microscopic Image Denoising Based on Fourth-Order PDEs and Contrast Enhancement

    Institute of Scientific and Technical Information of China (English)

    王瑜; 张慧妍

    2012-01-01

    A new denoising diffusion model is proposed for fluorescence microscopic images, in which fourth-order partial differential equations (PDEs) and contrast enhancement are utilized to overcome the blocky effect and false edges usually caused by second-order PDEs. Compared with second-order PDEs model, the proposed model shows superior performance in terms of both objective criteria and subjective human vision via processing simulated and experimental noisy images.%提出一种用于荧光显微图像去噪扩散模型的算法,该算法针对二阶偏微分方程去噪模型易引起的“块效应”和伪边缘等问题,采用正则化方法,利用四阶偏微分方程,同时融合对比度增强技术设计去噪模型.与二阶偏微分方程扩散模型相比,该算法不仅使去噪图像看起来更加自然清晰,而且在峰值信噪比和结构相似度等客观评价方法下也取得了更加满意的结果.

  1. Novel methods for multilinear data completion and de-noising based on tensor-SVD

    OpenAIRE

    Zhang, Zemin; Ely, Gregory; Aeron, Shuchin; Hao, Ning; Kilmer, Misha

    2014-01-01

    In this paper we propose novel methods for completion (from limited samples) and de-noising of multilinear (tensor) data and as an application consider 3-D and 4- D (color) video data completion and de-noising. We exploit the recently proposed tensor-Singular Value Decomposition (t-SVD)[11]. Based on t-SVD, the notion of multilinear rank and a related tensor nuclear norm was proposed in [11] to characterize informational and structural complexity of multilinear data. We first show that videos...

  2. Locally homogenized and de-noised vector fields for cardiac fiber tracking in DT-MRI images

    Science.gov (United States)

    Akhbardeh, Alireza; Vadakkumpadan, Fijoy; Bayer, Jason; Trayanova, Natalia A.

    2009-02-01

    In this study we develop a methodology to accurately extract and visualize cardiac microstructure from experimental Diffusion Tensor (DT) data. First, a test model was constructed using an image-based model generation technique on Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. These images were derived from a dataset having 122x122x500 um3 voxel resolution. De-noising and image enhancement was applied to this high-resolution dataset to clearly define anatomical boundaries within the images. The myocardial tissue was segmented from structural images using edge detection, region growing, and level set thresholding. The primary eigenvector of the diffusion tensor for each voxel, which represents the longitudinal direction of the fiber, was calculated to generate a vector field. Then an advanced locally regularizing nonlinear anisotropic filter, termed Perona-Malik (PEM), was used to regularize this vector field to eliminate imaging artifacts inherent to DT-MRI from volume averaging of the tissue with the surrounding medium. Finally, the vector field was streamlined to visualize fibers within the segmented myocardial tissue to compare the results with unfiltered data. With this technique, we were able to recover locally regularized (homogenized) fibers with a high accuracy by applying the PEM regularization technique, particularly on anatomical surfaces where imaging artifacts were most apparent. This approach not only aides in the visualization of noisy complex 3D vector fields obtained from DT-MRI, but also eliminates volume averaging artifacts to provide a realistic cardiac microstructure for use in electrophysiological modeling studies.

  3. Estimation of optimal PDE-based denoising in the SNR sense.

    Science.gov (United States)

    Gilboa, Guy; Sochen, Nir; Zeevi, Yehoshua Y

    2006-08-01

    This paper is concerned with finding the best partial differential equation-based denoising process, out of a set of possible ones. We focus on either finding the proper weight of the fidelity term in the energy minimization formulation or on determining the optimal stopping time of a nonlinear diffusion process. A necessary condition for achieving maximal SNR is stated, based on the covariance of the noise and the residual part. We provide two practical alternatives for estimating this condition by observing that the filtering of the image and the noise can be approximated by a decoupling technique, with respect to the weight or time parameters. Our automatic algorithm obtains quite accurate results on a variety of synthetic and natural images, including piecewise smooth and textured ones. We assume that the statistics of the noise were previously estimated. No a priori knowledge regarding the characteristics of the clean image is required. A theoretical analysis is carried out, where several SNR performance bounds are established for the optimal strategy and for a widely used method, wherein the variance of the residual part equals the variance of the noise.

  4. Incorporate $TV-l^{\\infty}$ model with Sparse Representations for Image Denoising, a post-processing approach

    OpenAIRE

    Zeng, Tieyong

    2006-01-01

    Sparse representations of images have revoked remarkable interest recently. The assumption that natural images admit a sparse decomposition over a redundant dictionary leads to efficient algorithm for image processing. In particular, the K-SVD method has been recently proposed and shown to perform very well for gray-scale and color image denoising task (\\cite{elada},\\cite{melada}). Meanwhile, the $TV-l^{\\infty}$ model with special choice of dictionary has been proved to be very effective for ...

  5. 基于MAP估计的复小波域局部自适应绝缘子红外热像去噪方法%Complex wavelet-domain local adaptive denoising method for insulator infrared thermal image based on MAP estimation

    Institute of Scientific and Technical Information of China (English)

    李佐胜; 姚建刚; 杨迎建; 刘云鹏; 李文杰

    2009-01-01

    为了从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出一种基于MAP估计的复小波域局部自适应去噪方法.首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量.实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,在有效去除图像噪声的同时,图像细节信息保留更完好.%In order to gain the real temperature distribution of insulator surface from infrared thermal image that is strongly interfered by white-noise, a complex wavelet-domain local adaptive denoising method based on maximum a posteriori (MAP) estimation is developed. It is confirmed for the first time that the dual tree complex wavelet transform (DT-CWT) coefficients of insulator infrared thermal image obey Laplacian distribution. The authors utilize the finest scaling sub-band coefficients of different filter banks to estimate their respective noise variances, and compute the signal variance of the coefficient using neighboring coefficients within a circular window whose radius varies with resolution, so noise-free coefficients are more accurately estimated by MAP estimation and the quality of the denoised image is improved. Experimental results demonstrate that the proposed method gets higher signal-to-noise rate (SNR), de-noises more effectively and preserves more detailed information of the original image than traditional Wiener filtering method, the adaptive Bayesian threshold methods based on discrete wavelet transform and DT-CWT.

  6. a Universal De-Noising Algorithm for Ground-Based LIDAR Signal

    Science.gov (United States)

    Ma, Xin; Xiang, Chengzhi; Gong, Wei

    2016-06-01

    Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

  7. Wavelet denoising for voxel-based compartmental analysis of peripheral benzodiazepine receptors with {sup 18}F-FEDAA1106

    Energy Technology Data Exchange (ETDEWEB)

    Shidahara, Miho; Ikoma, Yoko; Seki, Chie; Kanno, Iwao; Kimura, Yuichi [National Institute of Radiological Sciences, Biophysics Group, Molecular Imaging Center, Chiba (Japan); Fujimura, Yota; Ito, Hiroshi; Suhara, Tetsuya [National Institute of Radiological Sciences, Molecular Neuroimaging Group, Molecular Imaging Center, Chiba (Japan); Naganawa, Mika [National Institute of Radiological Sciences, Biophysics Group, Molecular Imaging Center, Chiba (Japan); Japan Society for the Promotion of Science, Tokyo (Japan)

    2008-02-15

    We evaluated the noise reduction capability of wavelet denoising for estimated binding potential (BP) images (k{sub 3}/k{sub 4}) of the peripheral benzodiazepine receptor using {sup 18}F-FEDAA1106 and nonlinear least-square fitting. Wavelet denoising within a three-dimensional discrete dual-tree complex wavelet transform was applied to simulate data and clinical dynamic positron emission tomography images of {sup 18}F-FEDAA1106. To eliminate noise components in wavelet coefficients, real and imaginary coefficients for each subband were thresholded individually using NormalShrink. A simulated dynamic brain image of {sup 18}F-FEDAA1106 was generated and Gaussian noise was added to mimic PET dynamic scan. The derived BP images were compared with true images using 156 rectangular regions of interest. Wavelet denoising was also applied to data derived from seven young normal volunteers. In the simulations, estimated BP by denoised image showed better correlation with the true BP values (Y = 0.83X + 0.94, r = 0.80), although no correlation was observed in the estimates between noise-added and true images (Y = 1.2X + 0.78, r = 0.49). For clinical data, there were visual improvements in the signal-to-noise ratio for estimated BP images. Wavelet denoising improved the bias and reduced the variation of pharmacokinetic parameters for BP. (orig.)

  8. New variational image decomposition model for simultaneously denoising and segmenting optical coherence tomography images.

    Science.gov (United States)

    Duan, Jinming; Tench, Christopher; Gottlob, Irene; Proudlock, Frank; Bai, Li

    2015-11-21

    Optical coherence tomography (OCT) imaging plays an important role in clinical diagnosis and monitoring of diseases of the human retina. Automated analysis of optical coherence tomography images is a challenging task as the images are inherently noisy. In this paper, a novel variational image decomposition model is proposed to decompose an OCT image into three components: the first component is the original image but with the noise completely removed; the second contains the set of edges representing the retinal layer boundaries present in the image; and the third is an image of noise, or in image decomposition terms, the texture, or oscillatory patterns of the original image. In addition, a fast Fourier transform based split Bregman algorithm is developed to improve computational efficiency of solving the proposed model. Extensive experiments are conducted on both synthesised and real OCT images to demonstrate that the proposed model outperforms the state-of-the-art speckle noise reduction methods and leads to accurate retinal layer segmentation.

  9. 二维离散小波变换滤波在医学图像去噪的应用研究%Research on Two Dimensional Discrete Wavelet Transform Denoising in Medical Image

    Institute of Scientific and Technical Information of China (English)

    王静

    2016-01-01

    医学图像降噪必须做到既降低图像噪声又保留图像细节。通过对二维离散小波变换滤波去噪的研究以及实验表明。采用硬阈值法时,在去噪过程中如果阈值选取太小,降噪后的图像仍然有噪声,如果阈值太大,重要图像特性被滤掉,会引起偏差。因此对于不同尺度的小波系数应该选取不同的阈值进行医学图像处理。%Medical image denoising must do both to reduce image noise and retain image details. Research based on the two-dimensional discrete wavelet transform denoising filter and experiment. The hard threshold method in denoising process, if the threshold is too small, the denoised image is still noise, if the threshold is too large, an important characteristic of image is filtered out, will cause the deviation. The wavelet coefficients of different scales should select different thresholds for medical image processing.

  10. Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV

    Science.gov (United States)

    Mortezanejad, R.; Gholami, A.

    2016-06-01

    One of the most crucial challenges in seismic data processing is the reduction of noise in the data or improving the signal-to-noise ratio (SNR). Wavelet- and curvelet-based denoising algorithms have become popular to address random noise attenuation for seismic sections. Wavelet basis, thresholding function, and threshold value are three key factors of such algorithms, having a profound effect on the quality of the denoised section. Therefore, given a signal, it is necessary to optimize the denoising operator over these factors to achieve the best performance. In this paper a general denoising algorithm is developed as a multi-variant (variable) filter which performs in multi-scale transform domains (e.g. wavelet and curvelet). In the wavelet domain this general filter is a function of the type of wavelet, characterized by its smoothness, thresholding rule, and threshold value, while in the curvelet domain it is only a function of thresholding rule and threshold value. Also, two methods, Stein’s unbiased risk estimate (SURE) and generalized cross validation (GCV), evaluated using a Monte Carlo technique, are utilized to optimize the algorithm in both wavelet and curvelet domains for a given seismic signal. The best wavelet function is selected from a family of fractional B-spline wavelets. The optimum thresholding rule is selected from general thresholding functions which contain the most well known thresholding functions, and the threshold value is chosen from a set of possible values. The results obtained from numerical tests show high performance of the proposed method in both wavelet and curvelet domains in comparison to conventional methods when denoising seismic data.

  11. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    KAUST Repository

    Reyes, Juan Carlos De los

    2013-11-01

    We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

  12. Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform.

    Science.gov (United States)

    Chitchian, Shahab; Mayer, Markus A; Boretsky, Adam R; van Kuijk, Frederik J; Motamedi, Massoud

    2012-11-01

    ABSTRACT. Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained.

  13. A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

    Science.gov (United States)

    Karimi, Davood; Ward, Rabab K.

    2016-03-01

    Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

  14. De-noising of Raman spectrum signal based on stationary wavelet transform

    Institute of Scientific and Technical Information of China (English)

    Qingwei Gao(高清维); Zhaoqi Sun(孙兆奇); Zhuoliang Cao(曹卓良); Pu Cheng(程蒲)

    2004-01-01

    @@ In this paper,the Raman spectrum signal de-noising based on stationary wavelet transform is discussed.Haar wavelet is selected to decompose the Raman spectrum signal for several levels based on stationarywavelet transform.The noise mean square σj is estimated by the wavelet details at every level,and thewavelet details toward 0 by a threshold σj √2lnn,where n is length of the detail,then recovery signalis reconstructed.Experimental results show this method not only suppresses noise effectively,but alsopreserves as many target characteristics of original signal as possible.This de-noising method offers a veryattractive alternative to Raman spectrum signal noise suppress.

  15. Shape-adaptive DCT for denoising of 3D scalar and tensor valued images.

    Science.gov (United States)

    Bergmann, Ørjan; Christiansen, Oddvar; Lie, Johan; Lundervold, Arvid

    2009-06-01

    During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.

  16. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

    Directory of Open Access Journals (Sweden)

    Qiyang Xiao

    2016-12-01

    Full Text Available In this study, a small leak detection method based on variational mode decomposition (VMD and ambiguity correlation classification (ACC is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF, an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM and back propagation neural network (BP methods.

  17. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

    Science.gov (United States)

    Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

    2016-12-13

    In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

  18. Regression model-based predictions of diel, diurnal and nocturnal dissolved oxygen dynamics after wavelet denoising of noisy time series

    Science.gov (United States)

    Evrendilek, F.; Karakaya, N.

    2014-06-01

    Continuous time-series measurements of diel dissolved oxygen (DO) through online sensors are vital to better understanding and management of metabolism of lake ecosystems, but are prone to noise. Discrete wavelet transforms (DWT) with the orthogonal Symmlet and the semiorthogonal Chui-Wang B-spline were compared in denoising diel, daytime and nighttime dynamics of DO, water temperature, pH, and chlorophyll-a. Predictive efficacies of multiple non-linear regression (MNLR) models of DO dynamics were evaluated with or without DWT denoising of either the response variable alone or all the response and explanatory variables. The combined use of the B-spline-based denoising of all the variables and the temporally partitioned data improved both the predictive power and the errors of the MNLR models better than the use of Symmlet DWT denoising of DO only or all the variables with or without the temporal partitioning.

  19. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

    Science.gov (United States)

    Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping

    2015-01-01

    Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

  20. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    Science.gov (United States)

    Ye, Linlin; Yang, Dan; Wang, Xu

    2014-06-01

    A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal.

  1. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

    Science.gov (United States)

    Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

    2015-10-23

    An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

  2. Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2016-07-01

    Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

  3. Wavelet transform-based methods for denoising of Coulter counter signals

    Science.gov (United States)

    Jagtiani, Ashish V.; Sawant, Rupesh; Carletta, Joan; Zhe, Jiang

    2008-06-01

    A process based on discrete wavelet transforms is developed for denoising and baseline correction of measured signals from Coulter counters. Given signals from a particular Coulter counting experiment, which detect passage of particles through a fluid-filled microchannel, the process uses a cross-validation procedure to pick appropriate parameters for signal denoising; these parameters include the choice of the particular wavelet, the number of levels of decomposition, the threshold value and the threshold strategy. The process is demonstrated on simulated and experimental single channel data obtained from a particular multi-channel Coulter counter processing. For these example experimental signals from 20 µm polymethacrylate and Cottonwood/Eastern Deltoid pollen particles and the simulated signals, denoising is aimed at removing Gaussian white noise, 60 Hz power line interference and low frequency baseline drift. The process can be easily adapted for other Coulter counters and other sources of noise. Overall, wavelets are presented as a tool to aid in accurate detection of particles in Coulter counters.

  4. Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

    Directory of Open Access Journals (Sweden)

    Wentao He

    2016-01-01

    Full Text Available Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

  5. Multispectral image filtering method based on image fusion

    Science.gov (United States)

    Zhang, Wei; Chen, Wei

    2015-12-01

    This paper proposed a novel filter scheme by image fusion based on Nonsubsampled ContourletTransform(NSCT) for multispectral image. Firstly, an adaptive median filter is proposed which shows great advantage in speed and weak edge preserving. Secondly, the algorithm put bilateral filter and adaptive median filter on image respectively and gets two denoised images. Then perform NSCT multi-scale decomposition on the de-noised images and get detail sub-band and approximate sub-band. Thirdly, the detail sub-band and approximate sub-band are fused respectively. Finally, the object image is obtained by inverse NSCT. Simulation results show that the method has strong adaptability to deal with the textural images. And it can suppress noise effectively and preserve the image details. This algorithm has better filter performance than the Bilateral filter standard and median filter and theirs improved algorithms for different noise ratio.

  6. Optimization of Wavelet-Based De-noising in MRI

    Directory of Open Access Journals (Sweden)

    K. Bartusek

    2011-04-01

    Full Text Available In the paper, a method for MR image enhancement using the wavelet analysis is described. The wavelet analysis is concentrated on the influence of threshold level and mother wavelet choices on the resultant MR image. The influence is expressed by the measurement and mutual comparison of three MT image parameters: signal to noise ratio, image contrast, and linear slope edge approximation. Unlike most standard methods working exclusively with the MR image magnitude, in our case both the MR image magnitude and the MR image phase were used in the enhancement process. Some recommendations are mentioned in conclusion, such as how to use a combination of mother wavelets with threshold levels for various types of MR images.

  7. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre......-image problem where denoised feature space points are mapped back into input space. This problem is inherently ill-posed due to the non-bijective feature space mapping. We present a semi-supervised denoising scheme based on kernel PCA and the pre-image problem, where class labels on a subset of the data points...... are used to improve the denoising. Moreover, by warping the Reproducing Kernel Hilbert Space (RKHS) we also account for the intrinsic manifold structure yielding a Kernel PCA basis that also benefit from unlabeled data points. Our two main contributions are; (1) a generalization of Kernel PCA...

  8. LMMSE Image Denoising Algorithm on Vector Space Model Context%矢量空间上下文模型的LMMSE图像去噪算法研究

    Institute of Scientific and Technical Information of China (English)

    蔡银珊

    2011-01-01

    Linear minimum mean square error estimation-LMMSE(wavelet-based multiscale linear minimum mean square-error estimation) is currently a hot topic of wavelet denoising field.Vector space discussed below is the minimum mean square error estimation of the new algorithm.Considering the image more the relationship between layers and the linear MMSE applied to the vector space,blurring the edges of the image denoising have been greatly improved.%线性最小均方误差估计LMMSE(wavelet-based multiscale linear minimum mean squareerror estimation)目前是小波去噪领域的热门课题,讨论了矢量空间的最小均方误差估计的新算法,主要考虑图像各层间的关系,将线性MMSE运用到矢量空间,对去噪图像边缘模糊问题有较大改善。

  9. Denoising and artefact reduction in dynamic flat detector CT perfusion imaging using high speed acquisition: first experimental and clinical results.

    Science.gov (United States)

    Manhart, Michael T; Aichert, André; Struffert, Tobias; Deuerling-Zheng, Yu; Kowarschik, Markus; Maier, Andreas K; Hornegger, Joachim; Doerfler, Arnd

    2014-08-21

    Flat detector CT perfusion (FD-CTP) is a novel technique using C-arm angiography systems for interventional dynamic tissue perfusion measurement with high potential benefits for catheter-guided treatment of stroke. However, FD-CTP is challenging since C-arms rotate slower than conventional CT systems. Furthermore, noise and artefacts affect the measurement of contrast agent flow in tissue. Recent robotic C-arms are able to use high speed protocols (HSP), which allow sampling of the contrast agent flow with improved temporal resolution. However, low angular sampling of projection images leads to streak artefacts, which are translated to the perfusion maps. We recently introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by joint bilateral filtering (JBF). As this edge-preserving noise reduction preserves streak artefacts, an empirical streak reduction (SR) technique is presented in this work. The SR method exploits spatial and temporal information in the form of total variation and time-curve analysis to detect and remove streaks. The novel approach is evaluated in a numerical brain phantom and a patient study. An improved noise and artefact reduction compared to existing post-processing methods and faster computation speed compared to an algebraic reconstruction method are achieved.

  10. Denoising and artefact reduction in dynamic flat detector CT perfusion imaging using high speed acquisition: first experimental and clinical results

    Science.gov (United States)

    Manhart, Michael T.; Aichert, André; Struffert, Tobias; Deuerling-Zheng, Yu; Kowarschik, Markus; Maier, Andreas K.; Hornegger, Joachim; Doerfler, Arnd

    2014-08-01

    Flat detector CT perfusion (FD-CTP) is a novel technique using C-arm angiography systems for interventional dynamic tissue perfusion measurement with high potential benefits for catheter-guided treatment of stroke. However, FD-CTP is challenging since C-arms rotate slower than conventional CT systems. Furthermore, noise and artefacts affect the measurement of contrast agent flow in tissue. Recent robotic C-arms are able to use high speed protocols (HSP), which allow sampling of the contrast agent flow with improved temporal resolution. However, low angular sampling of projection images leads to streak artefacts, which are translated to the perfusion maps. We recently introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by joint bilateral filtering (JBF). As this edge-preserving noise reduction preserves streak artefacts, an empirical streak reduction (SR) technique is presented in this work. The SR method exploits spatial and temporal information in the form of total variation and time-curve analysis to detect and remove streaks. The novel approach is evaluated in a numerical brain phantom and a patient study. An improved noise and artefact reduction compared to existing post-processing methods and faster computation speed compared to an algebraic reconstruction method are achieved.

  11. Denoising in Wavelet Domain Using Probabilistic Graphical Models

    Directory of Open Access Journals (Sweden)

    Maham Haider

    2016-11-01

    Full Text Available Denoising of real world images that are degraded by Gaussian noise is a long established problem in statistical signal processing. The existing models in time-frequency domain typically model the wavelet coefficients as either independent or jointly Gaussian. However, in the compression arena, techniques like denoising and detection, states the need for models to be non-Gaussian in nature. Probabilistic Graphical Models designed in time-frequency domain, serves the purpose for achieving denoising and compression with an improved performance. In this work, Hidden Markov Model (HMM designed with 2D Discrete Wavelet Transform (DWT is proposed. A comparative analysis of proposed method with different existing techniques: Wavelet based and curvelet based methods in Bayesian Network domain and Empirical Bayesian Approach using Hidden Markov Tree model for denoising has been presented. Results are compared in terms of PSNR and visual quality.

  12. Beltrami流及其在图像去噪中的应用%Beltrami flow and its application in image denoising

    Institute of Scientific and Technical Information of China (English)

    王泽龙; 朱炬波

    2012-01-01

    Partial differential equation (PDE) is one of the main methods for image processing and its significance is usually shown by the corresponding variable model, according to which the PDE can be optimized further to reach ideal results. Based on the classic Beltrami flow for image processing, a new metric tensor model on the image manifold is proposed for image denoising. The Beltrami flow with this metric tensor has clear geometrical significance, which induces the optimal selection method for parameters in the metric tensor. Meanwhile, this model provides a unified framework for the classic PDE based image denoising methods and the optimal selection method for its parameters makes the Beltrami flow have a better balance between smoothing the noise and preserving the edges. The experiment results show that the image denoising quality is greatly improved, especially for the images with abundant edges.%偏微分方程是图像处理主要的方法之一,一般通过其对应的变分模型给出方程的意义,并依此设计优化原偏微分方程以取得最佳的处理效果.针对图像去噪问题,基于经典的Beltrami几何流方法提出了图像流形上一种新的度量张量模型,具有该度量模型的Beltrami流具有清晰的几何意义,并依此提出了该度量模型中参数的优化选择方法.同时,该模型为经典的偏微分方程去噪方法提供了统一框架,且参数的优化选择使得Beltrami流在平滑噪声与边缘保持方面取得了良好的平衡.实验表明该方法提高了图像去噪效果,尤其是对于边缘丰富的图像效果更加明显.

  13. Method and application of wavelet shrinkage denoising based on genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Genetic algorithm (GA) based on wavelet transform threshold shrinkage (WTS) and translation-invafiant threshold shrinkage (TIS) is introduced into the method of noise reduction, where parameters used in WTS and TIS, such as wavelet function,decomposition levels, hard or soft threshold and threshold can be selected automatically. This paper ends by comparing two noise reduction methods on the basis of their denoising performances, computation time, etc. The effectiveness of these methods introduced in this paper is validated by the results of analysis of the simulated and real signals.

  14. 一种非零元个数约束的字典学习图像去噪算法%Image denoising algorithm of dictionary learning restricted by nonzeros number

    Institute of Scientific and Technical Information of China (English)

    段新涛; 岳冬利

    2011-01-01

    This paper presented a image denoising algorithm based on dictionary learning. It implemented dictionary learning by substituting error restriction for nonzeros number restriction on the base of K-SVD method. By analyzing effection on peak signal-to-noise ratio of denoising with different nonzeros number, it proposed implementing dictionary learning by using different number nonzero respectively for low-noise image and high-noise image to obtain sparsity represetation of image and restore original image. The results show that,comparing to the wavelet soft-threshold denoising method, our method has better ability of denoising under the premise of keeping the information of image edge and detail and has better vision effection.%提出了一种基于字典学习的图像去噪算法.在K-SVD字典学习算法的基础上,改变稀疏编码中误差约束为非零元个数约束来进行字典学习.在实验的基础上分析了使用不同非零元个数去噪时对峰值信噪比的影响,提出分别针对低噪图像和高噪图像采用两个固定非零元个数来进行字典学习,获得图像的稀疏表示,从而恢复出原始图像.实验结果表明,与小波软阈值去噪方法相比,本算法能够在保留图像边缘和细节信息的同时有效地去除图像中的噪声,具有较好的视觉效果.

  15. A volume-based method for denoising on curved surfaces

    KAUST Repository

    Biddle, Harry

    2013-09-01

    We demonstrate a method for removing noise from images or other data on curved surfaces. Our approach relies on in-surface diffusion: we formulate both the Gaussian diffusion and Perona-Malik edge-preserving diffusion equations in a surface-intrinsic way. Using the Closest Point Method, a recent technique for solving partial differential equations (PDEs) on general surfaces, we obtain a very simple algorithm where we merely alternate a time step of the usual Gaussian diffusion (and similarly Perona-Malik) in a small 3D volume containing the surface with an interpolation step. The method uses a closest point function to represent the underlying surface and can treat very general surfaces. Experimental results include image filtering on smooth surfaces, open surfaces, and general triangulated surfaces. © 2013 IEEE.

  16. A Novel De-noising Model Based on Independent Component Analysis and Beamlet Transform

    Directory of Open Access Journals (Sweden)

    Guangming Zhang

    2012-06-01

    Full Text Available Vehicle video key frame processing as an important part of intelligent transportation systems plays a significant role. Traditional vehicle video key frame extraction often has lots of noises, it can’t meet the requirements of the recognition and tracking. In this paper, a novel method which is combined independent component analysis with beamlet transform is proposed. Firstly, a random matrix was produce to separate the key frame into a separated image for estimate. Then beamlet transform was applied to optimize the coefficients. At last, the coefficients were selected for image reconstruction by inverse of the beamlet transform. By contrast, this approach could remove more noises and reserve more details, and the efficiency of our approach is better than other traditional de-noising approaches.

  17. Wavelet-based Image Enhancement Using Fourth Order PDE

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren

    2011-01-01

    The presence of noise interference signal may cause problems in signal and image analysis; hence signal and image de-noising is often used as a preprocessing stage in many signal processing applications. In this paper, a new method is presented for image de-noising based on fourth order partial...... differential equations (PDEs) and wavelet transform. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate coefficients of the image unchanged. These coefficients have the main information of the image. Since noise affects both...... indicate superiority of the proposed method over the existing waveletbased image de-noising, anisotropic diffusion, and wiener filtering techniques....

  18. 基于四阶PDE的去噪方法在煤矿井下视频监控图像中的应用%Denoising Method for Application in Coal Mine Monitoring Image Based on Fourth-order Partial Differential Equations

    Institute of Scientific and Technical Information of China (English)

    巩文迪; 刘佳毅; 卢兆林

    2013-01-01

    The final monitoring images in video monitoring system are usually in low contrast and brightness because of special application environment in underground mine. A improved denoising method based fourth-order Partial Differential Equations (PDE) is proposed in this paper. The proposed method segments the image domain into speckle domain and non-speckle domain, then uses different conductance coefficients depending on the domain type. Comparing to traditional anisotropic diffusion method, the simulation results show that this method will suppress the noise so as to achieve a better image quality and visual effect. At the same time, it can effectively remove isolated speckles, eliminates flash point ,keeps edge details and line texture in the image.%煤矿井下特殊的应用环境直接影响着图像的质量,导致图像视觉效果较差。文章提出一种改进的基于四阶PDE的去噪方法,该方法将图像区域分为闪点区域和非闪点区域,并针对不同的区域采用不同的传导系数。与传统的各向异性扩散方法相比较,仿真实验结果表明,该方法在抑制噪声的同时能够有效克服二阶偏微分方程引起的块效应、消除闪点并保持图像的边缘、线条纹理等细节,从而达到更好的图像质量和视觉效果。

  19. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants

    Directory of Open Access Journals (Sweden)

    Ahmad Shaheryar

    2016-01-01

    Full Text Available Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative neural network (AANN based empirical sensor models have widely been reported for sensor calibration monitoring. However, such ill-posed data driven models may result in poor generalization and robustness. To address above-mentioned issues, several regularization heuristics such as training with jitter, weight decay, and cross-validation are suggested in literature. Apart from these regularization heuristics, traditional error gradient based supervised learning algorithms for multilayered AANN models are highly susceptible of being trapped in local optimum. In order to address poor regularization and robust learning issues, here, we propose a denoised autoassociative sensor model (DAASM based on deep learning framework. Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture. In order to improve robustness, dropout heuristic and domain specific data corruption processes are exercised during unsupervised pretraining phase. The proposed sensor model is trained and tested on sensor data from a PWR type nuclear power plant. Accuracy, autosensitivity, spillover, and sequential probability ratio test (SPRT based fault detectability metrics are used for performance assessment and comparison with extensively reported five-layer AANN model by Kramer.

  20. 基于Rudin-Osher-Fatemi模型的图像除模糊和除噪音新模型%A Model for Image Deblurring and Denoising Based on the Rudin-Osher-Fatemi Model

    Institute of Scientific and Technical Information of China (English)

    石玉英; 常谦顺

    2006-01-01

    介绍一种依赖时间的新模型来解决图像除噪音和除模糊问题.分别使用逆反射、中值两种边界条件的数值试验比较本文新模型和Rudin-Osher-Fatemi模型.试验结果表明中值边界条件的误差比逆反射边界条件误差小.%A time dependent model for deblurring and denoising problems is proposed. Comparisons with the Rudin-Osher Fatemi model are made in numerical experiments with antireflective boundary conditions and medium boundary conditions. The results demonstrate that the error with medium boundary conditions is smaller than that with antireflective boundary conditions.

  1. 基于分数阶偏微分方程和 CB 模型的彩色图像去噪方法%Novel Color Image Denoising Method Based on Fractional-Order Partial Differential Equation and CB Model

    Institute of Scientific and Technical Information of China (English)

    周千

    2016-01-01

    将分数阶偏微分理论和 CB 模型相结合应用于图像去噪,提出了一种基于分数阶偏微分方程和 CB 模型的彩色图像去噪方法。首先,将一副彩色图像分解为色度 C 和亮度 B 两部分,然后用分数阶偏微分模型处理亮度 B ,而对于色度C ,由于其受到单位长度的限制,在处理时非常困难,利用拉格朗日乘数法并通过添加辅助变量,将色度转化为两个近似的子问题,从而得到色度的近似处理方法,最后将处理后的亮度 B 和色度 C 合成为新的彩色图像。最后通过实验证明了该方法的有效性。%Combing fractional-order differential theory with Chromaticity-Brightness(CB) model ,a novel image denois-ing model was proposed ,which was based on fractional-order partial differential equation and CB model .Firstly ,a color im-age was decomposed into chromaticity component and brightness component .Secondly ,fractional-order differential model was used for brightness component .For chromaticity component ,Lagrange multipliers method was used and an auxiliary variable was added to approximate the chromaticity .Thirdly ,the retorted image was got by multiplying the recovered chro-maticity with recovered brightness .Finally ,it proved the validity of the proposed model through the experiment .

  2. Noise Removal From Microarray Images Using Maximum a Posteriori Based Bivariate Estimator

    Directory of Open Access Journals (Sweden)

    A.Sharmila Agnal

    2013-01-01

    Full Text Available Microarray Image contains information about thousands of genes in an organism and these images are affected by several types of noises. They affect the circular edges of spots and thus degrade the image quality. Hence noise removal is the first step of cDNA microarray image analysis for obtaining gene expression level and identifying the infected cells. The Dual Tree Complex Wavelet Transform (DT-CWT is preferred for denoising microarray images due to its properties like improved directional selectivity and near shift-invariance. In this paper, bivariate estimators namely Linear Minimum Mean Squared Error (LMMSE and Maximum A Posteriori (MAP derived by applying DT-CWT are used for denoising microarray images. Experimental results show that MAP based denoising method outperforms existing denoising techniques for microarray images.

  3. Improved image denoising algorithm in digital diffusion-reaction filter%一种改进的反应扩散滤波器图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    宋宜美; 冯纪强

    2012-01-01

    A new digital nonlinear diffusion-reaction filter model is proposed based on Nordstrom energy functional for image denoising. Numerical results show that the model is not only effective in denoising images with Gaussian noise, salt and pepper or Speckle noise, but also suitable for images with mixed noise. The denoised image with good visual effect has higher peak signal noise ratio.%提出一种基于Nordstr(o)m能量泛函的非线性反应扩散数字滤波模型.数值实验结果表明该模型不仅对高斯噪声、椒盐噪声和Speckle噪声污染的图像去噪效果较好,而且也适合对混合噪声污染的图像去噪.去噪后图像有良好的视觉效果,峰值信噪比较高.

  4. COMPARISON OF MEAT IMAGE DENOISING ALGORITHMS BASED ON PARTIAL DIFFERENTIAL EQUATION%基于偏微分方程的肉品图像去噪对比研究

    Institute of Scientific and Technical Information of China (English)

    贾渊; 刘鹏程; 蒋勇; 彭增起

    2011-01-01

    Meat product contains moisture contents as well as fat chips and connective tissues produced during the cutting process, so the noise is common in its image under visible light. The algorithms based on partial differential equation model can remove the noise as well as keep certain features of the image, the widely used models are the Perona-Malik model, the ROF total variation model and the Y-K fourth-order model. In this paper we compared these three models in their signal-to-noise ratios, method noise and operating time by mixing the Gauss noise and the spiced salt noise with the Lena image,and on this basis we selected the pork image as denosing object and compared the performance of these three models. Experimental result showed that the ROF model is superior in maintaining the image details when removing the noise than other two models, the Y-K fourth-order partial differential equation model can remove the noise but with blurring image, and the P-M model is the worst among the three in removing the meat image noise.%肉品中含有水分,或者切割过程中产生的脂肪碎屑或结缔组织,其可见光图像容易产生噪声.基于偏微分方程模型的算法去噪同时能够保持图像的某些特征,常见的有Perona-Malik模型、全变差ROF模型、Y-K四阶模型.通过对Lena图像加入高斯噪声和椒盐噪声,对比了三种模型的信噪比、方法噪声及运算时间.在此基础上,以猪肉图像为去噪对象,比较三种算法的性能.结果表明:ROF模型在去除噪声的同时,保持细节的能力强于其他两种模型,YK四阶偏微分方程模型能够去除噪声,但是图像模糊.去噪效果最差的是P-M模型.

  5. 基于提升小波的方向扩散算法实现侧扫声纳图像去噪%The directional diffusion based on discrete wavelet algorithm to achieve side-scan sonar image denoising

    Institute of Scientific and Technical Information of China (English)

    赵四能; 张丰; 杜震洪; 刘仁义; 刘南

    2012-01-01

    Side-scan sonar technology has been widely used as a major submarine detection method. However, the image noise through strictly improved still can't be eliminated completely, which decreases the accuracy of image interpretation. Four methods are examined to reduce or eliminate the side-scan sonar image noise, including directional diffusion based on the partial differential equations, regularization PM (AOS algorithm) , directional diffusion based on discrete wavelet, directional diffusion based on lifting wavelet. Compared with traditional methods, such as mean filtering, median filtering, Wiener filtering, wavelet soft threshold, hard threshold and Bayesian method of estimating threshold, it is found that the directional diffusion based on lifting wavelet method can not only improve the PSNR, but also maintain smoothness index and edge-preserving index. It is more suitable for side-scan sonar image denoising.%侧扫声纳作为一种重要的海底探测技术,已得到广泛的使用,但声强数据经过严格的数据处理后,依然存在噪声,影响图像的正确判读.针对侧扫声纳图像中的噪声问题,采用基于偏微分方程的方向扩散、正则化P-M(AOS算法)、基于离散小波的方向扩散、基于提升小波的方向扩散4种方法,与传统的均值滤波、中值滤波、维纳滤波和小波软阈值、硬阈值、贝叶斯估计阈值的方法进行实验对比,发现基于提升小波的方向扩散方法,不仅能有效提高峰值信噪比,而且还能保持较好的平滑指数和边缘保持指数,更适用于侧扫声纳图像的去噪处理.

  6. Threshold Optimized Wavelet for Remotely Sensed Image Denoising%阈值优化的遥感影像小波去噪

    Institute of Scientific and Technical Information of China (English)

    刘晓莉; 任丽秋; 李伟; 王小国; 胡忠威

    2016-01-01

    To solve the problems of denoising limitation,noise residue and noise misjudgment of traditional wavelet threshold in removing noise of remote sensing image,an optimization algorithm of wavelet threshold function in view of the remote sensing image was brought forward.The algorithm made use of wavelet edge detection algorithm to determine the wavelet coefficients of remote sensing image edge character.Then,according to the noise variance,it set optimized threshold function to remove noise,in other words,it modified the noise variance and made it vary with the decomposition scale based on the previous unified threshold.The algorithm preserved advantages of the traditional soft threshold and hard threshold, improved their defects,and generated a new threshold function,while making the new threshold more flexible in the processing of wavelet coefficients.After optimization of wavelet threshold denoising,the smooth remote sensing image could be obtained. Finally,the remote sensing image of wavelet edge detection was embedded into the smooth remote sensing image.The experimental results show that compared with the traditional wavelet threshold denoising method,the algorithm could solve the denoising problems in the traditional threshold function,keep the details of the remote sensing image in the course of removing noise,and improve the signal-to-noise ratio.%针对传统的小波阈值在去除遥感影像噪声时存在噪声残留和噪声误判的问题,提出了针对遥感影像的小波阈值函数优化算法.该算法利用小波边缘检测算法确定遥感影像边缘特征的小波系数,然后根据噪声的方差设置优化的阈值函数去噪,即在以往的统一阈值基础上加以修改,使阈值能随着分解尺度的变化而改变,对传统的软阈值和硬阈值的优点予以保留,改进它们的缺点,生成一种新的阈值函数,使它在处理小波系数时更加灵活.经过优化的小波阈值去噪后得到平滑遥感影像,之

  7. A SVD Based Image Complexity Measure

    DEFF Research Database (Denmark)

    Gustafsson, David Karl John; Pedersen, Kim Steenstrup; Nielsen, Mads

    2009-01-01

    Images are composed of geometric structures and texture, and different image processing tools - such as denoising, segmentation and registration - are suitable for different types of image contents. Characterization of the image content in terms of geometric structure and texture is an important...... problem that one is often faced with. We propose a patch based complexity measure, based on how well the patch can be approximated using singular value decomposition. As such the image complexity is determined by the complexity of the patches. The concept is demonstrated on sequences from the newly...... collected DIKU Multi-Scale image database....

  8. Study on Application of Partial Differential Equation in Image Denoising%偏微分方程在图像去噪中的应用研究

    Institute of Scientific and Technical Information of China (English)

    周晓峰

    2013-01-01

    图像学术领域一直都很注重对图像去噪的研究,随着科学技术的发展,偏微分模型开始应用于图像去噪。本文主要介绍偏微分方程在图像去噪中的应用历程,对几种常用的图像去噪进行了比较科学详细的论述,分析了各方法的优势和劣势,这次研究对偏微分方程未来的发展方向指明了方向。%The image academic field has been paying close attention to image denoising. With the development of science and technology, partial differential model has been applied in image denoising. This article mainly introduces the application of partial differential equation in image denoising, presenting scientific and detailed introduction of some commonly used image denoising. The advantages and disadvantages of different methods are analyzed. This study points the way for partial differential equation development.

  9. 基于混合阶偏微分方程的超声图像降噪%Method of ultrasound image denoising based on hybrid-order PDE

    Institute of Scientific and Technical Information of China (English)

    张宏群; 陈小晴; 陶兴龙

    2013-01-01

    In this paper, a hybrid-order PDE method based on the total variation and a fourth - order PDE is proposed to reduce the speckle noise in ultrasound images. A fidelity term constructed by noise distribution is used to enhance the edge of the image, when the hybrid-order partial differential equation is added as regular item. Despeckiing experiments on testing images and medical ultrasound images show that the proposed method has higher capability of noise reduction and image edge details preserving. The results show that the new method has better performance than the total variation model and the LLT model in respects of PSNR: peak signal to noise ratio, MSE: mean square error and operating efficiency.%针对偏微分方程在医学超声图像处理中的斑点噪声滤除问题,在全变分去噪模型和四阶LLT降噪模型的基础上,提出一种针对医学超声图像的混合阶变分降噪方法.该方法引入二阶全变分和四阶偏微分的混合阶偏微分方程作为正则项,并利用斑点噪声分布构建保真项.用标准测试数据和真实数据对所提混合阶变分降噪模型进行验证,试验结果表明,该模型在有效滤除超声图像斑点噪声的同时,能较好地保护图像的边缘和纹理细节信息.处理后的图像在峰值信噪比PSNR、均方误差MSE、运行效率方面均优于全变分和LLT模型.

  10. Sinogram denoising via simultaneous sparse representation in learned dictionaries

    Science.gov (United States)

    Karimi, Davood; Ward, Rabab K.

    2016-05-01

    Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster.

  11. Method for signal decomposition and denoising based on nonuniform cosine-modulated filter banks

    Institute of Scientific and Technical Information of China (English)

    Xuemei Xie; Li Li; Guangming Shi; Bin Peng

    2008-01-01

    In this paper,a novel method for signal decomposition and denoising is proposed based on a nonuniform filter bank (NUFB),which is derived from a uniform filter bank.With this method,the signal is firstly decomposed into M subbands using a uniform filter bank.Then according to their energy distribution,the corresponding consecutive filters are merged to compose the nonuniform filters.With the resulting NUFB,the signal can be readily matched and flexibly decomposed according to its power spectrum distribution.As another advantage,this method can be used to detect and remove the narrow-band noise from the corrupted signal.To verify the proposed method,a simulation of extracting the main information of an audio signal and removing its glitch is given.

  12. Implemented Wavelet Packet Tree based Denoising Algorithm in Bus Signals of a Wearable Sensorarray

    Science.gov (United States)

    Schimmack, M.; Nguyen, S.; Mercorelli, P.

    2015-11-01

    This paper introduces a thermosensing embedded system with a sensor bus that uses wavelets for the purposes of noise location and denoising. From the principle of the filter bank the measured signal is separated in two bands, low and high frequency. The proposed algorithm identifies the defined noise in these two bands. With the Wavelet Packet Transform as a method of Discrete Wavelet Transform, it is able to decompose and reconstruct bus input signals of a sensor network. Using a seminorm, the noise of a sequence can be detected and located, so that the wavelet basis can be rearranged. This particularly allows for elimination of any incoherent parts that make up unavoidable measuring noise of bus signals. The proposed method was built based on wavelet algorithms from the WaveLab 850 library of the Stanford University (USA). This work gives an insight to the workings of Wavelet Transformation.

  13. A cross-correlation based fiber optic white-light interferometry with wavelet transform denoising

    Science.gov (United States)

    Wang, Zhen; Jiang, Yi; Ding, Wenhui; Gao, Ran

    2013-09-01

    A fiber optic white-light interferometry based on cross-correlation calculation is presented. The detected white-light spectrum signal of fiber optic extrinsic Fabry-Perot interferometric (EFPI) sensor is firstly decomposed by discrete wavelet transform for denoising before interrogating the cavity length of the EFPI sensor. In measurement experiment, the cross-correlation algorithm with multiple-level calculations is performed both for achieving the high measurement resolution and for improving the efficiency of the measurement. The experimental results show that the variation range of the measurement results was 1.265 nm, and the standard deviation of the measurement results can reach 0.375 nm when an EFPI sensor with cavity length of 1500 μm was interrogated.

  14. Discrete directional wavelet bases for image compression

    Science.gov (United States)

    Dragotti, Pier L.; Velisavljevic, Vladan; Vetterli, Martin; Beferull-Lozano, Baltasar

    2003-06-01

    The application of the wavelet transform in image processing is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions. Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show very interesting gains compared to the standard two-dimensional analysis.

  15. Fast image coding approach with denoising based on fractal and DWT%带降噪的快速DWT与分形相结合图像编码方法

    Institute of Scientific and Technical Information of China (English)

    刘波; 房斌; 罗棻; 张世勇

    2011-01-01

    A new image coding algorithm is presented, which combined the fractal and discrete wavelet transform, based on the statistical character of image blocks, the distance between the value block and its best matched domain block in the baseline fractal coding and the distribution of noise unknown. In the proposed algorithm, ifthe robust regression between the value block and it' s best matched domain block is less than the given threshold value, the value block is compressed by fractal coding, otherwise is compressed by discrete waveform transform. Simulation results show that the proposed algorithm can speed up encoding time greatly and improve on the quality of the reconstructed image. Especially that has good robustness against the outliers caused by salt and pepper noise.%针对图像易受外界噪声干扰,且这种噪声分布通常是未知的这一问题,结合图像的统计特性,基本分形编码中值域块和最佳匹配的定义域块之间的距离统计特性等,提出一种基于方差不变特性、邻域搜索的分形与小波相结合的图像分形编码算法.在该算法中,如果值域决和最佳匹配之间的稳健回归优化目标函数取值小于给定的阈值,则用分形压缩算法编码该块,否则用小波变换压缩该块.实验结果表明,该方法可使编码速度比基本分形算法有较大提高,而且原始图像在受到外界干扰的情况下,该算法表现出了较好的鲁棒特性.

  16. Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

    Science.gov (United States)

    Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

    2015-01-01

    Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

  17. A spatio-temporal filtering approach to denoising of single-trial ERP in rapid image triage.

    Science.gov (United States)

    Yu, Ke; Shen, Kaiquan; Shao, Shiyun; Ng, Wu Chun; Kwok, Kenneth; Li, Xiaoping

    2012-03-15

    Conventional search for images containing points of interest (POI) in large-volume imagery is costly and sometimes even infeasible. The rapid image triage (RIT) system which is a human cognition guided computer vision technique is potentially a promising solution to the problem. In the RIT procedure, images are sequentially presented to a subject at a high speed. At the instant of observing a POI image, unique POI event-related potentials (ERP) characterized by P300 will be elicited and measured on the scalp. With accurate single-trial detection of such unique ERP, RIT can differentiate POI images from non-POI images. However, like other brain-computer interface systems relying on single-trial detection, RIT suffers from the low signal-to-noise ratio (SNR) of the single-trial ERP. This paper presents a spatio-temporal filtering approach tailored for the denoising of single-trial ERP for RIT. The proposed approach is essentially a non-uniformly delayed spatial Gaussian filter that attempts to suppress the non-event related background electroencephalogram (EEG) and other noises without significantly attenuating the useful ERP signals. The efficacy of the proposed approach is illustrated by both simulation tests and real RIT experiments. In particular, the real RIT experiments on 20 subjects show a statistically significant and meaningful average decrease of 9.8% in RIT classification error rate, compared to that without the proposed approach.

  18. 基于偏微分方程的彩色图像乘性去噪模型%A Multiplicative Denoising Model of Color Images Based on Partial Differential Equations

    Institute of Scientific and Technical Information of China (English)

    尹云光; 曹杨; 郭志昌; 李静

    2011-01-01

    提出一种新的非线性偏微分方程恢复模型,解决含乘性噪声的彩色图像恢复问题.新模型中引入光滑后向量值图像的几何流形特征判断图像的"真"、"假"边缘,利用P(x)-Laplace扩散系数在区域内部和边界以不同的方式进行扩散,利用|u|q扩散系数在乘性噪声干扰不同的区域以不同的速度进行扩散.理论分析和数值实验结果表明了该模型的有效性.%A nonlinear partial differential equation model was proposed to remove multiplicative noise of color images. The geometrical manifold characteristics of vector-valued image after smoothing are introduced to distinguish the “real” and “false” edges. The p (x)-Laplace diffusion coefficients drive image to diffuse in different ways on the edges and in the domains of images. The | u |q diffusion coefficient drives image to diffuse at different speeds that fit to the multiplicative noises strength. Theoretical analysis and numerical results show the efficiency of the new method.

  19. The Hilbert-Huang Transform-Based Denoising Method for the TEM Response of a PRBS Source Signal

    Science.gov (United States)

    Hai, Li; Guo-qiang, Xue; Pan, Zhao; Hua-sen, Zhong; Khan, Muhammad Younis

    2016-08-01

    The denoising process is critical in processing transient electromagnetic (TEM) sounding data. For the full waveform pseudo-random binary sequences (PRBS) response, an inadequate noise estimation may result in an erroneous interpretation. We consider the Hilbert-Huang transform (HHT) and its application to suppress the noise in the PRBS response. The focus is on the thresholding scheme to suppress the noise and the analysis of the signal based on its Hilbert time-frequency representation. The method first decomposes the signal into the intrinsic mode function, and then, inspired by the thresholding scheme in wavelet analysis; an adaptive and interval thresholding is conducted to set to zero all the components in intrinsic mode function which are lower than a threshold related to the noise level. The algorithm is based on the characteristic of the PRBS response. The HHT-based denoising scheme is tested on the synthetic and field data with the different noise levels. The result shows that the proposed method has a good capability in denoising and detail preservation.

  20. 稀疏性正则化的图像泊松去噪算法%Image Poisson Denoising Using Sparse Representations

    Institute of Scientific and Technical Information of China (English)

    孙玉宝; 韦志辉; 吴敏; 肖亮; 费选

    2011-01-01

    The removal of Poisson noise is essential in medical and astromical imaging. In the framework of Bayesian-MAP estimation, a sparsity regularized convex functional model is proposed to denoise Poisson noisy image in terms of the sparse representation of the underlying image in an over-complete dictionary. The negative-log Poisson likelihood functional is used for data fidelity term and non-smooth regularization term constrains the sparse representations of the underlying image over the dictionary. An additional term is also added in the functional to ensure the non-negative of the denoised image. Based on the Split Bergman iteration method,a multi-step fast iterative algorithm is proposed to solve the above model numerically. By introducing an intermediate variable and Bergman distance,the original problem is transformed into solving two simple sub-problem iterafively, thus the compurational complexity is decreased rapidly. Experimental results demonstrate the effectiveness of our recovery model and the munetical iteration algorithm.%去除医学、天文图像中的泊松噪声是一个重要问题,基于图像在过完备字典下的稀疏表示,在BayesianMAP框架下建立了稀疏性正则化的图像泊松去噪凸变分模型,采用负log的泊松似然函数作为模型的数据保真项,模型中非光滑的正则项约束图像表示系数的稀疏性,并附加非负件约束,保证去噪图像的非负性.基于分裂Bregman方法,提出了数值求解该模型的多步迭代快速算法,通过引入辅助变量与Bregman距离可将原问题转化为两个简单子问题的迭代求解,降低了计算复杂性.实验结果验证了本文模型与数值算法的有效性.

  1. Image Denoising and Segmentation Basedon Mutual Information Criterion%基于互信息准则的图像平滑和分割

    Institute of Scientific and Technical Information of China (English)

    温铁祥; 潘正洋; 辜嘉

    2014-01-01

    Scalespace play an important role in many computer vision tasks. Automatic scale selection is the foundation of multi-scale image analysis, but its performance is still very subjective and empirical. To automatically select the appropriate scale for a particular application, a scale selection model based on information theory was proposed in this paper. The proposed model utilizes the mutual information as a measuring criterion of similarity for the optimal scale selection in multi-scale analysis, with applications to the image denoising and segmentation. Firstly, the multi-scale image smoothing and denoising method based on the morphological operator was studied. This technique does not require the prior knowledge of the noise variance and can effectively eliminate the changes of illumination. Secondly, a clustering-based unsupervised image segmentation algorithm was developed by recursively pruning the Huffman coding tree. The proposed clustering algorithm can preserve the maximum amount of information at a speciifc clustering number from the information-theoretical point of view. Finally, for the feasibility of the proposed algorithms, its theoretical properties were analyzed mathematically and its performance was tested through a series of experiments, which demonstrate that it yields the optimal scale for the developed image denoising and segmentation algorithms.%在计算机视觉领域,尺度空间扮演着一个很重要的角色。多尺度图像分析的基础是自动尺度选择,但它的性能非常主观和依赖于经验。基于互信息的度量准则,文章提出了一种自动选取最优尺度的模型。首先,研究专注于基于形态学算子的多尺度图像平滑去噪方法,这种技术不需要噪声方差的先验知识,可以有效地消除照度的变化。其次,通过递归修剪Huffman编码树,设计了一个基于聚类的无监督图像分割算法。一个特定的聚类数从信息理论的角度来看,提

  2. 一种新颖的Contourlet域中子辐射图像降噪方法%A Novel Method of Neutron Radiography Image Denoising Using Contourlet Transform

    Institute of Scientific and Technical Information of China (English)

    金炜; 魏彪; 潘英俊; 冯鹏; 唐彬

    2006-01-01

    由于受CCD相机、中子散射及控制电路等因素的影响,数字中子照相系统所获图像常被噪音污染,抑制噪音对于提高数字中子照相系统图像质量具有重要意义.利用多尺度几何分析能捕获图像几何结构的特性,提出一种新颖的基于contourlet变换的图像去噪方法.通过计算方差一致性测度(VHM),确定局部自适应窗口,从而最优估计contourlet系数的阈值萎缩因子,对contourlet系数进行萎缩,实现降噪功能.该方法将阈值去噪法与基于子带相关的图像去噪法相结合,充分利用在同一方向子带中沿边缘或轮廓contourlet系数的相关性,它能实现"去噪"和"保留信号"之间的平衡.实验结果表明,该方法在峰值信噪比指标上优于传统的contourlet系数硬阈值处理方法及维纳滤波方法,能有效地抑制图像噪音,同时适合于中子辐射图像的处理.%A new image transform, namely, the contourlet transform is introduced,which can capture the intrinsic geometrical structure of image. Furthermore, a novel image denoising scheme based on contourlet is presented. Via calculating variance homogeneous measurement (VHM), the locally adaptive window is determined to estimate the shrinkage factor optimally, then the contourlet coefficient is shrunk using the shrinkage factor. The scheme utilizing the correlation of contourlet coefficients in the same subband along the edge or contour of the image, which can get the tradeoff between "noises removing" and "details preserving". In numerical comparisons with various methods, the presented scheme outperforms the traditional contourlet denoising method based on hard-thresholding and Wiener filter in terms of PSNR. Experiments also show that this scheme could not only remove the noises effectively, but also suit for the neutron radiography system.

  3. Adaptive de-noising method based on wavelet and adaptive learning algorithm in on-line PD monitoring

    Institute of Scientific and Technical Information of China (English)

    王立欣; 诸定秋; 蔡惟铮

    2002-01-01

    It is an important step in the online monitoring of partial discharge (PD) to extract PD pulses from various background noises. An adaptive de-noising method is introduced for adaptive noise reduction during detection of PD pulses. This method is based on Wavelet Transform (WT) , and in the wavelet domain the noises decomposed at the levels are reduced by independent thresholds. Instead of the standard hard thresholding function, a new type of hard thresholding function with continuous derivative is employed by this method. For the selection of thresholds, an unsupervised learning algorithm based on gradient in a mean square error (MSE) is present to search for the optimal threshold for noise reduction, and the optimal threshold is selected when the minimum MSE is obtained. With the simulating signals and on-site experimental data processed by this method,it is shown that the background noises such as narrowband noises can be reduced efficiently. Furthermore, it is proved that in comparison with the conventional wavelet de-noising method the adaptive de-noising method has a better performance in keeping the pulses and is more adaptive when suppressing the background noises of PD signals.

  4. Denoising of Ictal EEG Data Using Semi-Blind Source Separation Methods Based on Time-Frequency Priors.

    Science.gov (United States)

    Hajipour Sardouie, Sepideh; Bagher Shamsollahi, Mohammad; Albera, Laurent; Merlet, Isabelle

    2015-05-01

    Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source separation approaches, namely the Time-Frequency-Generalized EigenValue Decomposition (TF-GEVD) and the Time-Frequency-Denoising Source Separation (TF-DSS), for the denoising of ictal signals based on these time-frequency signatures. The performance of the proposed methods is compared with that of CCA and Independent Component Analysis (ICA) approaches for the denoising of simulated ictal EEGs and of real ictal data. The results show the superiority of the proposed methods in comparison with CCA and ICA.

  5. Multi-Quadratic Dynamic Programming Procedure of - Preserving Denoising for Medical Images

    Science.gov (United States)

    Pham, C. T.; Kopylov, A. V.

    2015-05-01

    In this paper, we present a computationally efficient technique for edge preserving in medical image smoothing, which is developed on the basis of dynamic programming multi-quadratic procedure. Additionally, we propose a new non-convex type of pair-wise potential functions, allow more flexibility to set a priori preferences, using different penalties for various ranges of differences between the values of adjacent image elements. The procedure of image analysis, based on the new data models, significantly expands the class of applied problems, and can take into account the presence of heterogeneities and discontinuities in the source data, while retaining high computational efficiency of the dynamic programming procedure and Kalman filterinterpolator. Comparative study shows, that our algorithm has high accuracy to speed ratio, especially in the case of high-resolution medical images.

  6. Image denoising model in combination withpartial differential equation and median filtering%融合偏微分方程和中值滤波的图像去噪模型

    Institute of Scientific and Technical Information of China (English)

    万山; 李磊民; 黄玉清

    2011-01-01

    针对基于偏微分方程(PDE)的图像去噪模型不能有效地去除脉冲噪声,并且低阶偏微分方程在去噪的同时会出现“块效应”现象的问题,提出一种融合偏微分方程和自适应中值滤波的图像去噪模型.该模型通过对图像梯度的分析,在梯度变化剧烈区域和梯度变化微小区域利用二阶模型去噪以提高去噪效率;而在梯度渐变区域利用四阶模型平滑图像以避免出现“块效应”现象.同时,利用脉冲噪声梯度值远大于边缘梯度值的特点,定位脉冲噪声所在区域,在该区域利用自适应中值滤波消除脉冲噪声.该方法能有效去除脉冲噪声,保护图像边缘并消除“块效应”现象,同时提高了去噪效率.实验表明了该模型的有效性.%The denoising model based on Partial Differential Equation (PDE) model cannot eliminate impulse noise and low-order PDE will produce blocky effect. In order to solve these problems, a denoising model combining PDE and adaptive median filtering was proposed. Through analyzing the image gradient, this model used second order model to denoise at the region with obvious gradient change and the region with tiny gradient change. At the smooth region, fourth order model was used to denoise. The region of the impulse noise was localized by making use of the characteristic that the gradient of the impulse noise is far bigger than the gradient of the edge. At this region, the adaptive median filtering was used to eliminate impulse noise. This method can eliminate impulse noise and protect the image edge effectively. It also can overcome the blocky effect and improve the denoising efficiency. The experiments prove the validity of the model.

  7. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    Science.gov (United States)

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.

  8. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

    Directory of Open Access Journals (Sweden)

    Hao Sun

    2014-01-01

    Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

  9. Compression and denoising in magnetic resonance imaging via SVD on the Fourier domain using computer algebra

    Science.gov (United States)

    Díaz, Felipe

    2015-09-01

    Magnetic resonance (MR) data reconstruction can be computationally a challenging task. The signal-to-noise ratio might also present complications, especially with high-resolution images. In this sense, data compression can be useful not only for reducing the complexity and memory requirements, but also to reduce noise, even to allow eliminate spurious components.This article proposes the use of a system based on singular value decomposition of low order for noise reconstruction and reduction in MR imaging system. The proposed method is evaluated using in vivo MRI data. Rebuilt images with less than 20 of the original data and with similar quality in terms of visual inspection are presented. Also a quantitative evaluation of the method is presented.

  10. About Advances in Tensor Data Denoising Methods

    Directory of Open Access Journals (Sweden)

    Salah Bourennane

    2008-10-01

    Full Text Available Tensor methods are of great interest since the development of multicomponent sensors. The acquired multicomponent data are represented by tensors, that is, multiway arrays. This paper presents advances on filtering methods to improve tensor data denoising. Channel-by-channel and multiway methods are presented. The first multiway method is based on the lower-rank (K1,…,KN truncation of the HOSVD. The second one consists of an extension of Wiener filtering to data tensors. When multiway tensor filtering is performed, the processed tensor is flattened along each mode successively, and singular value decomposition of the flattened matrix is performed. Data projection on the singular vectors associated with dominant singular values results in noise reduction. We propose a synthesis of crucial issues which were recently solved, that is, the estimation of the number of dominant singular vectors, the optimal choice of flattening directions, and the reduction of the computational load of multiway tensor filtering methods. The presented methods are compared through an application to a color image and a seismic signal, multiway Wiener filtering providing the best denoising results. We apply multiway Wiener filtering and its fast version to a hyperspectral image. The fast multiway filtering method is 29 times faster and yields very close denoising results.

  11. OCT图像降噪混合滤波方法%Mixed filtering method for OCT image denoising

    Institute of Scientific and Technical Information of China (English)

    李佳; 王笑梅

    2011-01-01

    通过对光学相干层析(OCT)系统中的噪音源进行分析,提出一种混合滤波处理方法,对OCT图像进行降噪处理.利用小波变换的原理将含噪图像进行小波分解,得到高频和低频的子信号,保留低频近似图像信号,分别对水平、垂直和对角3个方向的高频信号采用均值滤波,并将之前保留的低频近似图像信号与滤波后的这3个方向上的信号合成得到去噪后的图像.实验结果表明,该算法在降低噪声的同时尽可能的保留了图像细节,取得了良好的降噪效果.%Through the analysis of noise sources of optical coherence tomography (OCT) system, a mixed filtering method for noise reduction on the OCT image is put forward.Firstly, the noisy image is to be decomposed to the high frequency and low fiequency subsignals in accordance with the principle of wavelet transformation, and then retains the low-frequency approximate image signal, using mean filter respectively to horizontal, vertical and diagonal high-frequency signals in three directions.Finally, the denoised image will be obtained by the integration of the filtered image signals from the low-frequency proximate signals and signals filtered from the three directions.Experimental results show that, in reducing the noise, the method retains image details and achieves a good noise reduction result.

  12. Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard;

    2012-01-01

    procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...... base these illustrations on two fMRI BOLD data sets — one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe....

  13. Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

    Directory of Open Access Journals (Sweden)

    Torsten Bölke

    2014-11-01

    Full Text Available Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.

  14. A Fast Alternating Minimization Algorithm for Nonlocal Vectorial Total Variational Multichannel Image Denoising

    Directory of Open Access Journals (Sweden)

    Rubing Xi

    2014-01-01

    Full Text Available The variational models with nonlocal regularization offer superior image restoration quality over traditional method. But the processing speed remains a bottleneck due to the calculation quantity brought by the recent iterative algorithms. In this paper, a fast algorithm is proposed to restore the multichannel image in the presence of additive Gaussian noise by minimizing an energy function consisting of an l2-norm fidelity term and a nonlocal vectorial total variational regularization term. This algorithm is based on the variable splitting and penalty techniques in optimization. Following our previous work on the proof of the existence and the uniqueness of the solution of the model, we establish and prove the convergence properties of this algorithm, which are the finite convergence for some variables and the q-linear convergence for the rest. Experiments show that this model has a fabulous texture-preserving property in restoring color images. Both the theoretical derivation of the computation complexity analysis and the experimental results show that the proposed algorithm performs favorably in comparison to the widely used fixed point algorithm.

  15. 一种改进的双变量收缩模型图像去噪%An improved image denoising method for bivariate shrinkage model

    Institute of Scientific and Technical Information of China (English)

    李向军; 姜玉莉

    2014-01-01

    An improved image denoising method for local adaptive bivariate shrinkage model is proposed in this paper ac-cording to the characteristics that the low-frequency subband of noise image contains noise. The high-frequency subband is de-noised by locally adaptive bivariate shrinkage model,and the residual low-frequency subband is denoised by locally adaptive Gaussian model. This method can reflect both the clustering performance of intra-scale and the correlation of inter-scale,and has good local adaptive property. The discrete wavelet transform was used to denoise in a experiment. The experimental results show the improved algorithm is more superior to the classical methods in both PSNR and subjective visual effect.%针对噪声图像低频子带含有噪声的特点,给出了一种改进的局部自适应双变量收缩模型的图像去噪算法,对于高频子带用局部自适应双变量模型进行去噪,而对低频子带用具有局部自适应的高斯模型进行去噪。该算法既体现了尺度内的聚类性,又体现了尺度间的相关性且具有很好的局部自适应性,在实验中用离散小波变换进行去噪。实验结果表明,这种改进的算法无论从峰值信噪比,还是从主观视觉效果上都要优于传统的去噪算法。

  16. 基于偏微分方程的混合噪声去噪研究%Mixed noise denoised based on partial differential equation

    Institute of Scientific and Technical Information of China (English)

    杨农丰; 吴成茂; 屈汉章

    2013-01-01

    针对目前图像去噪方法存在的主要缺陷是仅适用于单一噪声的滤除,无法解决图像混合噪声去噪的问题,提出一种加权混合噪声模型,建立其能量泛函表达式,利用变分法获得其欧拉一拉格朗日方程并给出其显式差分迭代求解算法.通过对其数值算法的改进,不仅提高了该模型数值算法的速度和稳定性,而且在一定程度上避免了降噪后图像的阶梯效应.仿真实验表明,加权混合噪声去噪算法在去除混合噪声的同时更好地保留了图像的细节信息,其降噪性能相比现有方法有一定程度的改善.%As the most major drawback of the present image denoising methods is only appropriate for the single noise removal and can not solve the mixed denoising problem.This paper presented a weighting mixed noise model and established the energy functional expression of the model,then used variational method to get the Euler-Lagrange equation of the model.Furthermore,it proposed an improved iterative algorithm to solve the proposed model based on explicit difference iteration regularization method.The improved numerical algorithm can not only enhance the speed and the stability of numerical algorithm,but also avoid the staircase effect of the image denoising in some way.The simulation results show that the proposed model can effectively smooth the noises and preserve the edge and fine detail information properly,while comparing with the traditional algorithm,noise reduction performance is improved at some level.

  17. Numerical Implementation of Adaptive Fidelity Term Denoising Algorithm Based on Total Variation%基于全变分自适应保真项去噪算法的数值实现

    Institute of Scientific and Technical Information of China (English)

    王宏志; 刘婉军; 韩啸

    2014-01-01

    On the basis of the classical algorithm of the image denoising based on total variation,a numerical algorithm of total variation based on adaptive fidelity term was proposed. Different intensities of denoising were used to avoid the shortages of the traditional method and then numerical method was chosen so as to realize our algorithm.On the premise of the classical total variation,our method made up for the shortcomings of the original ladder and excessive smoothing effect,especially for the image denoising of fine texture and detail images,it made the remain of most of their image characteristics.Our treatment can be applied to a series of image processing based on partial differential equations simply.%基于全变差图像去噪经典算法,提出一种自适应保真项的数值实现算法。该算法利用图像纹理区和光滑区中噪声的不同特点,采用不同去噪强度避免传统方法的不足,并以数值方法实现。在保持经典算法去噪效果的前提下,解决了原有阶梯效应和过度平滑的问题,尤其对精致的纹理和细节图像,使其在去噪的同时,不丢失图像特点。该方法处理相对简单,可应用于以偏微分方程为基础的图像处理。

  18. 选择性计算的快速非局部均值图像去噪%Fast Nonlocal Means Image Denoising Algorithm Using Selective Calculation

    Institute of Scientific and Technical Information of China (English)

    罗学刚; 吕俊瑞; 王华军; 杨强

    2015-01-01

    A fast nonlocal means (NLM) image denoising method with selective calculation is proposed to solve the problem that the computational cost of similarity weights is high. By using L2 Norm successive elimination, a large number of pixels of low similarity van be rejected through a small amount of additive operations on integral image, and the massive calculation on measuring similarity can be effectively reduced. According to spatial coherence in the image domain, an approach for adaptive search area based on patch geodesic distance is proposed. Experimental results demonstrate that the proposed method, compared with the state-of-the-art algorithms, can not only accelerate the nonlocal means algorithm, but also elevate the image quality.%针对非局部均值(NLM)图像去噪算法度量像素间的相似性计算强度高的问题,提出了一种选择性计算的快速NLM去噪方法。在图像块像素灰度值向量空间距离计算时,利用L2范数逐次消元法,只需在图像积分图上通过少量加法运算即可剔除大量相似性低的像素点,有效地减少计算强度。根据图像空间相关性强的特点,提出了基于patch测地线距离的动态调整搜索区域的方法。实验结果表明,与其他经典算法相比,该方法获得了较好的加速,也提升了NLM算法的去噪性能。

  19. 带BesOV忠诚项的图像去噪变分模型%Variational model with the Besov fidelity term for image denoising

    Institute of Scientific and Technical Information of China (English)

    张伟斌; 冯象初; 王卫卫

    2011-01-01

    对基于Besov空间的图像去噪模型,利用Besov空间B12,2与Sobolev空间H1的等价关系,引入用Besov模刻画梯度变化的忠诚项,从而得到一类新的图像去噪变分模型.给出了相应的基于小波的数值算法,不需要处理非线性偏微分方程,是一种高效的快速算法.数值实验表明新模型能够获得很好的去噪效果,同时还能够保持图像的边缘和细节.%An improved variational model based on Besov space for image denoising is proposed.The L2 fidelity term in the classical model is generalized to the Besov fidelity term.The new model can be quickly solved by using the wavelet thresholding algorithm.The proposed algorithm does not need to solve the nonlinear partial differential equation, so it is an efficient and fast algorithm.Numerical experiments show that this model can remove noise efficiently while preserving the texture and the details.

  20. Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

    Directory of Open Access Journals (Sweden)

    Guanghua Zhang

    2016-09-01

    Full Text Available This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection.

  1. A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

    Directory of Open Access Journals (Sweden)

    Dongsheng Fang

    2016-10-01

    Full Text Available An interferometric synthetic aperture radar (InSAR phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l 1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes.

  2. Classical low-pass filter and real-time wavelet-based denoising technique implemented on a DSP: a comparison study

    Science.gov (United States)

    Dolabdjian, Ch.; Fadili, J.; Huertas Leyva, E.

    2002-11-01

    We have implemented a real-time numerical denoising algorithm, using the Discrete Wavelet Transform (DWT), on a TMS320C3x Digital Signal Processor (DSP). We also compared from a theoretical and practical viewpoints this post-processing approach to a more classical low-pass filter. This comparison was carried out using an ECG-type signal (ElectroCardiogram). The denoising approach is an elegant and extremely fast alternative to the classical linear filters class. It is particularly adapted to non-stationary signals such as those encountered in biological applications. The denoising allows to substantially improve detection of such signals over Fourier-based techniques. This processing step is a vital element in our acquisition chain using high sensitivity magnetic sensors. It should enhance detection of cardiac-type magnetic signals or magnetic particles in movement.

  3. Integration of speckle de-noising and image segmentation using Synthetic Aperture Radar image for flood extent extraction

    Indian Academy of Sciences (India)

    J Senthilnath; H Vikram Shenoy; Ritwik Rajendra; S N Omkar; V Mani; P G Diwakar

    2013-06-01

    Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

  4. 基于广义高斯混合模型的图像加权平均滤波去噪方法%Generalized Gaussian Mixture Model and Weighted Average Image Filter Denoising

    Institute of Scientific and Technical Information of China (English)

    孔晨燕; 谢从华; 苏剑峰; 于丹

    2012-01-01

      To remove the trailing noise, histogram fuzzy based filter denoising methods often have the problems of image blurring and residual noisy. To address this problem, the authors of this paper propose a new image de⁃noising method based on Generalized Gaussian Mixture (GGM) model and weighted average image filter. Firstly, the generalized Gaussian mixture model for image is constructed. Secondly, the noise data is determined accord⁃ing to the feature differences between this point and its neighbors. Finally, a weighted average filter is construct⁃ed by the GGM to build an image denoising. Histogram based filter and classical partial differential equation method are compared with the proposed method. Experimental results show that the method has a better denois⁃ing effect than the other methods.%  基于直方图的模糊滤波方法对图像的拖尾噪声去噪会导致图像模糊、残留的噪声较多等问题,本文提出一种新的基于广义高斯混合模型的图像去噪方法。首先,建立图像的广义高斯分布及其有限混合模型;其次,通过像素周围点特征值的变化范围确定噪声数据;最后,利用广义高斯函数构建一个加权平均滤波器进行图像去噪。对基于直方图的滤波方法、经典的偏微分方程和本文方法进行比较实验,结果表明本文方法具有更好的去噪效果。

  5. Applying oriented fourth-order partial-differential equations to fluorescence microscopic image denoising%基于方向四阶偏微分方程的荧光显微图像去噪

    Institute of Scientific and Technical Information of China (English)

    王瑜; 薛红

    2012-01-01

    荧光显微图像由于光学成像系统的自身物理缺陷,光电转换,样本组织结构以及人为误差等因素的影响,噪声无法避免,为此,一种基于方向四阶偏微分方程的荧光显微图像去噪方法被提出,主要考虑两个方面,一是基于变分方法,二是控制滤波模型的扩散方向.在人工合成和真实荧光显微图像上进行的实验结果表明,同传统二阶偏微分方程扩散模型相比,应用所提出的方法进行去噪,不管是主观视觉,还是客观评价,均表现出了更好的性能.%Noise in the fluorescence microscopic images can' t be avoided because of imperfect optical imaging system, photoelectric conversion, specimen tissue structure and human errors etc during the course of optical imaging. Therefore, a new denoising method is proposed based on oriented fourth-order partial-differential equations (PDEs) for fluorescence microscopic images, in which two aspects are considered. One is based on variational method, the other is based on controlling diffusion directioa Experimental results show that the proposed method not only makes the denoised images subjectively more natural and clearer, but also achieves better performance in terms of objective criterion such as peak signal to noise ratio (PSNR) and the structural similarity (SSIM) compared with the related second-order PDEs diffusion models.

  6. Bayesian inference on multiscale models for poisson intensity estimation: applications to photon-limited image denoising.

    Science.gov (United States)

    Lefkimmiatis, Stamatios; Maragos, Petros; Papandreou, George

    2009-08-01

    We present an improved statistical model for analyzing Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main contributions include: 1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the noisy observed Poisson data (counts); 2) extension of the method to work under a multiscale hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling interscale coefficient dependencies in the vicinity of image edges; 3) exploration of a 2-D recursive quad-tree image representation, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary-tree image representation involving beta-mixture rate-ratio densities; and 4) a novel multiscale image representation, which we term Poisson-Haar decomposition, that better models the image edge structure, thus yielding improved performance. Experimental results on standard images with artificially simulated Poisson noise and on real photon-limited images demonstrate the effectiveness of the proposed techniques.

  7. Comparison of Wavelet Filters in Image Coding and Denoising using Embedded Zerotree Wavelet Algorithm

    Directory of Open Access Journals (Sweden)

    V. Elamaran

    2012-12-01

    Full Text Available In this study, we present Embedded Zerotree Wavelet (EZW algorithm to compress the image using different wavelet filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal and to remove noise by setting appropriate threshold value while decoding. Compression methods are important in telemedicine applications by reducing number of bits per pixel to adequately represent the image. Data storage requirements are reduced and transmission efficiency is improved because of compressing the image. The EZW algorithm is an effective and computationally efficient technique in image coding. Obtaining the best image quality for a given bit rate and accomplishing this task in an embedded fashion are the two problems addressed by the EZW algorithm. A technique to decompose the image using wavelets has gained a great deal of popularity in recent years. Apart from very good compression performance, EZW algorithm has the property that the bitstream can be truncated at any point and still be decoded with a good quality image. All the standard wavelet filters are used and the results are compared with different thresholds in the encoding section. Bit rate versus PSNR simulation results are obtained for the image 256x256 barbara with different wavelet filters. It shows that the computational overhead involved with Daubechies wavelet filters but are produced better results. Like even missing details i.e., higher frequency components are picked by them which are missed by other family of wavelet filters.

  8. De-Noising Ultrasound Images of Colon Tumors Using Daubechies Wavelet Transform

    Science.gov (United States)

    Moraru, Luminita; Moldovanu, Simona; Nicolae, Mariana Carmen

    2011-10-01

    In this paper, we present a new approach to analysis of the cancer of the colon in ultrasonography. A speckle suppression method was presented. Daubechies wavelet transform is used due to its approximate shift invariance property and extra information in imaginary plane of complex wavelet domain when compared to real wavelet domain. The methods that we propose have provided quite satisfactory results and show the usefulness of image processing techniques in the diagnosis by means of medical imaging. Local echogenicity variance of ROI is utilized so as to compare with local echogenicity distribution within entire acquired image. Also the image was analyzed using the histogram which interprets the gray-level of images. Such information is valuable for the discrimination of tumors. The aim of this work is not the substitution of the specialist, but the generation of a series of parameters which reduce the need of carrying out the biopsy.

  9. Joint denoising and distortion correction of atomic scale scanning transmission electron microscopy images

    OpenAIRE

    Berkels, Benjamin; Wirth, Benedikt

    2016-01-01

    Nowadays, modern electron microscopes deliver images at atomic scale. The precise atomic structure encodes information about material properties. Thus, an important ingredient in the image analysis is to locate the centers of the atoms shown in micrographs as precisely as possible. Here, we consider scanning transmission electron microscopy (STEM), which acquires data in a rastering pattern, pixel by pixel. Due to this rastering combined with the magnification to atomic scale, movements of th...

  10. Log-Gabor Feature-Based Nonlocal Means Denoising Algorithm and Its Acceleration Scheme%基于Log-Gabor特征的非局部均值去噪算法及其加速方案研究

    Institute of Scientific and Technical Information of China (English)

    张嵩; 景华炯

    2015-01-01

    非局部均值是一种基于像素长程相似性的图像空域去噪算法,它一般采用灰度块特征估计图像像素间的相似度。文中首先使用基于Log-Gabor特征的像素间相似度估计获得较好的去噪效果。然后将Log-Gabor几何特征与灰度特征相融合,所形成的混合相似度具有更佳的图像局部自适应性,去噪性能也得到进一步提升。最后基于Johnson-Lindenstrauss引理研究利用随机降维方法降低相似度计算的复杂度,并对该加速方案的效果,包括降维前后运行时间对比、降维程度以及随机矩阵生成方法对去噪性能的影响,进行详细试验分析,结果证明基于随机降维的加速方案的有效性。%The nonlocal means ( NLM) is a spatial domain image denoising method, and it exploits long range similarities between pixels of natural images. Notably, the similarity between true pixel values in original NLM is estimated based on patch information of noise-corrupted input image. In this paper, the pixel similarities in NLM are estimated based on Log-Gabor features to achieve good denoising results. Moreover, the mixed similarity combining the Log-Gabor features with intensity information is exploited to get better adaptivity to local image characteristics and further improve the denoising quality. In addition, the random projection-based NLM speed-up method is studied based on Johnson-Lindenstrauss lemma. Extensive tests including the running time comparison before and after dimensionality reduction, the impact of types of projection matrices and the extent of dimensionality reduction on final denoising performances are carried out. The experimental results confirm the effectiveness of the proposed acceleration scheme.

  11. 一种改进的去噪阈值混合滤波算法%A hybrid filtering algorithm based on denoising threshold

    Institute of Scientific and Technical Information of China (English)

    周绍光; 贾凯华; 王港淼; 刘会珍

    2013-01-01

    中值滤波和均值滤波通常被分别用来处理脉冲噪声和高斯噪声,但当图像同时存在高斯噪声和脉冲噪声时,单独用任何一种滤波方法都不能达到最好的去噪效果.针对这一问题,本文提出了一种改进的基于去噪阈值的图像混合滤波算法,可以更有效地减少噪声,又可以较好地保持图像的边缘细节信息.%Median filter and average filter are usually used to process impulse noise and Gaussian noise respectively.But when image is corrupted by Gaussian noise and impulse noise simultaneously,good filtering effect cannot be obtained if only using median filter or average filter.In allusion to this question,an improved hybrid image filtering based on the denoising threshold was proposed in this paper.This method would suppress noise efficiently and preserve edge details of image at the same time.

  12. Patch-wise denoising of phase fringe patterns based on matrix enhancement

    Science.gov (United States)

    Kulkarni, Rishikesh; Rastogi, Pramod

    2016-12-01

    We propose a new approach for the denoising of a phase fringe pattern recorded in an optical interferometric setup. The phase fringe pattern which is generally corrupted by high amount of speckle noise is first converted into an exponential phase field. This phase field is divided into a number of overlapping patches. Owing to the small size of each patch, the presence of a simple structure of the interference phase is assumed in it. Accordingly, the singular value decomposition (SVD) of the patch allows us to separate the signal and noise components effectively. The patch is reconstructed only with the signal component. In order to further improve the robustness of the proposed method, an enhanced data matrix is generated using the patch and the SVD of this enhanced matrix is computed. The matrix enhancement results in an increased dimension of the noise subspace which thus accommodates more amount of noise component. Reassignment of the filtered pixels of the preceding patch in the current patch improves the noise filtering accuracy. The fringe denoising capability in function of the noise level and the patch size is studied. Simulation and experimental results are provided to demonstrate the practical applicability of the proposed method.

  13. Nonlinear Second-Order Partial Differential Equation-Based Image Smoothing Technique

    Directory of Open Access Journals (Sweden)

    Tudor Barbu

    2016-09-01

    Full Text Available A second-order nonlinear parabolic PDE-based restoration model is provided in this article. The proposed anisotropic diffusion-based denoising approach is based on some robust versions of the edge-stopping function and of the conductance parameter. Two stable and consistent approximation schemes are then developed for this differential model. Our PDE-based filtering technique achieves an efficient noise removal while preserving the edges and other image features. It outperforms both the conventional filters and also many PDE-based denoising approaches, as it results from the successful experiments and method comparison applied.

  14. A weighted dictionary learning model for denoising images corrupted by mixed noise.

    Science.gov (United States)

    Liu, Jun; Tai, Xue-Cheng; Huang, Haiyang; Huan, Zhongdan

    2013-03-01

    This paper proposes a general weighted l(2)-l(0) norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

  15. An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix

    Directory of Open Access Journals (Sweden)

    Yushun Liu

    2016-06-01

    Full Text Available Due to electromagnetic interference in power substations, the partial discharge (PD signals detected by ultrahigh frequency (UHF antenna sensors often contain various background noises, which may hamper high voltage apparatus fault diagnosis and localization. This paper proposes a novel de-noising method based on the generalized S-transform and module time-frequency matrix to suppress noise in UHF PD signals. The sub-matrix maximum module value method is employed to calculate the frequencies and amplitudes of periodic narrowband noise, and suppress noise through the reverse phase cancellation technique. In addition, a singular value decomposition de-noising method is employed to suppress Gaussian white noise in UHF PD signals. Effective singular values are selected by employing the fuzzy c-means clustering method to recover the PD signals. De-noising results of simulated and field detected UHF PD signals prove the feasibility of the proposed method. Compared with four conventional de-noising methods, the results show that the proposed method can suppress background noise in the UHF PD signal effectively, with higher signal-to-noise ratio and less waveform distortion.

  16. Rigorous mathematical investigation of a nonlinear anisotropic diffusion-based image restoration model

    Directory of Open Access Journals (Sweden)

    Tudor Barbu

    2014-06-01

    Full Text Available A nonlinear diffusion based image denoising technique is introduced in this paper. The proposed PDE denoising and restoration scheme is based on a novel diffusivity function that uses an automatically detected conductance parameter. A robust mathematical treatment is also provided for our anisotropic diffusion model. We demonstrate that edge-stopping function model is properly chosen, explaining the mathematical reasons behind it. Also, we perform a rigorous mathematical investigation on of the existence and uniqueness of the solution of our nonlinear diffusion equation. This PDE-based noise removal approach outperforms most diffusion-based methods, producing considerably better smoothing results and providing a much better edge preservation.

  17. A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2017-02-01

    Full Text Available Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs. Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA. The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE and percent root mean square difference (PRD respectively, and increased 40.36% in signal-to-noise ratio improvement (SNRimp compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual

  18. HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

    Directory of Open Access Journals (Sweden)

    Qingjiao Sun

    2016-01-01

    Full Text Available Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR pathological image enhancement method based on improved bias field correction and guided image filter (GIF. Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

  19. Entropy-Based Method of Choosing the Decomposition Level in Wavelet Threshold De-noising

    Directory of Open Access Journals (Sweden)

    Yan-Fang Sang

    2010-06-01

    Full Text Available In this paper, the energy distributions of various noises following normal, log-normal and Pearson-III distributions are first described quantitatively using the wavelet energy entropy (WEE, and the results are compared and discussed. Then, on the basis of these analytic results, a method for use in choosing the decomposition level (DL in wavelet threshold de-noising (WTD is put forward. Finally, the performance of the proposed method is verified by analysis of both synthetic and observed series. Analytic results indicate that the proposed method is easy to operate and suitable for various signals. Moreover, contrary to traditional white noise testing which depends on “autocorrelations”, the proposed method uses energy distributions to distinguish real signals and noise in noisy series, therefore the chosen DL is reliable, and the WTD results of time series can be improved.

  20. 基于线性最小均方误差估计的 SAR 图像降噪%SAR image denoising via linear minimum meansquare error estimation

    Institute of Scientific and Technical Information of China (English)

    刘书君; 吴国庆; 张新征; 沈晓东; 李勇明

    2016-01-01

    针对合成孔径雷达(synthetic aperture radar,SAR)图像降噪过程中容易引起细节纹理信息损失的问题,该文结合 SAR 图像相干斑噪声的统计特性,提出了一种基于变换域系数线性最小均方误差(linear mini-mum mean-square error,LMMSE)估计的 SAR 图像降噪方法。首先通过 SAR 场景下的 Kmeans 聚类算法将相似图像块聚类;然后针对每一类相似图像块集合进行奇异值分解(singular value decomposition,SVD),得到同时包含图像块集合行列相关信息的含噪奇异值系数;为从含噪奇异值系数中更准确地估计出真实图像奇异值的系数,先通过加性独立信号噪声(additive signal-dependent noise,ASDN)模型将乘性噪声转化为加性噪声,再利用LMMSE 准则对奇异值系数进行估计,最后将估计结果重构得到降噪后的图像块集合。实验结果表明,该方法充分利用相似图像块集合奇异值系数稀疏的特性,采用 LMMSE 准则估计奇异值系数,既保证了系数中噪声分量的去除又避免了图像纹理细节对应小系数的丢失,不仅去噪效果明显,同时能有效地保持图像纹理细节,具有良好的图像视觉效果。%In order to solve the problem that many detail texture information is lost during the synthetic ap-erture radar (SAR)image denoising process,SAR image denoising approach based on the estimated transform domain coefficients by the means of linear minimum mean square error (LMMSE)is proposed,which combines the statistical characteristics of the speckle noise in the SAR image.Firstly,cluster image blocks into disjoint sets of similar blocks through Kmeans corresponding to the SAR scene.Secondly,perform singular value de-composition (SVD)for each set of similar blocks,and the noisy singular value coefficients containing the corre-lation of rows and columns of the set of similar blocks can be obtained.In order to estimate the noise

  1. Denoised and texture enhanced MVCT to improve soft tissue conspicuity

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, Ke, E-mail: ksheng@mednet.ucla.edu; Qi, Sharon X. [Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States); Gou, Shuiping [Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Xidian University, Xi’An 710071 (China); Wu, Jiaolong [Xidian University, Xi’An 710071 (China)

    2014-10-15

    Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT). Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast-to-noise ratios (CNRs) were quantified. Results: By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult-to-see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions-of-interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion

  2. AN ADAPTIVE OPTIMISATION METHOD BASED ON RPCA VIDEO DENOISING%基于 RPCA 视频去噪算法的自适应优化方法

    Institute of Scientific and Technical Information of China (English)

    李小利; 杨晓梅

    2016-01-01

    传统去噪算法不能在尽量滤除噪声的同时很好地保持原始图像信息。针对这种情况,提出基于鲁棒主成分分析的自适应视频去噪算法。首先根据视频数据的低秩性和噪声的稀疏性,利用加速近端梯度方法重建出原始视频的低秩部分和稀疏部分,实现噪声的初步分离;其次利用自适应中值滤波器进行预滤波处理,提高块匹配精度,进一步去除视频噪声;最后引入自适应奇异值阈值法,增强图像细节边缘信息,降低迭代优化算法的时间复杂度。实验结果表明,该方法不仅能极大程度地恢复出原始视频序列,还能自适应地去除干扰噪声。不论从客观指标 PSNR 值还是从主观视觉,该方法与传统去噪方法相比都具有很大的优势。%Traditional denoising algorithm cannot well reserve primitive image information while filtering the noise as much as possible.In light of this situation,the paper presents an RPCA-based adaptive video denoising algorithm.First,according to the low-rank property of video data and the sparsity of noise,it utilises the accelerated proximal gradient approach to reconstruct the low-rank component and sparse component of original video,and realises the initial separation of the noise.Then,it uses adaptive median filter to make pre-processing of filtration to improve block matching accuracy,and further removes video noise.Finally,it introduces adaptive singular-value threshold method to enhance the detailed edge information of image,reduces the time complexity of iterative optimisation algorithm.It is demonstrated by experimental result that the proposed algorithm can restore original video sequence to a great deal extent,besides it can also adaptively remove interference noise.The algorithm has significant advantage no matter in objective quantitative indicator PSNR or subjective vision quality compared with traditional denoising algorithms.

  3. A New Matlab De-noising Algorithm for Signal Extraction

    Institute of Scientific and Technical Information of China (English)

    ZHANG Fu-ming; WU Song-lin

    2007-01-01

    The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab′s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.

  4. VIDEO DENOISING USING SWITCHING ADAPTIVE DECISION BASED ALGORITHM WITH ROBUST MOTION ESTIMATION TECHNIQUE

    Directory of Open Access Journals (Sweden)

    V. Jayaraj

    2010-08-01

    Full Text Available A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed algorithm is that an appropriate filter is used for replacing the corrupted pixel based on the estimation of the noise variance present in the filtering window. This leads to reduced blurring and better fine detail preservation even at the high mixed noise density. It performs both spatial and temporal filtering for removal of the noises in the filter window of the videos. The Improved Cross Diamond Search Motion Estimation technique uses Least Median Square as a cost function, which shows improved performance than other motion estimation techniques with existing cost functions. The results show that the proposed algorithm outperforms the other algorithms in the visual point of view and in Peak Signal to Noise Ratio, Mean Square Error and Image Enhancement Factor.

  5. FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

    Science.gov (United States)

    Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

    2016-07-12

    In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

  6. 基于特征均值的SVD信号去噪算法%MEAN VALUE OF EIGENVALUE-BASED SVD SIGNAL DENOISING ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    王益艳

    2012-01-01

    根据矩阵奇异值分解厚理,提出基于特征均值的信号去噪算法.该算法首先构造出加噪信号的Hankel矩阵,并对其进行SVD变换,再将小于全体特征值的均值的那些特征值置零,最后通过SVD反交换重建出去噪后的信号.通过与传统小波和FFT 信号去噪算法进行对比实验.结果表明,该方法具有较强的噪声鲁棒性,同时能更好地保留信号细节,但实现速度有所降低.%A signal denoising algorithm based on mean value of eigenvalue is proposed, which is according to the principle of matrix singular value decomposition. Firstly, this algorithm constructs a Hankel matrix with noised signal, and conducts SVD transformation on it; then it sets those eigenvalues to zero of which they are less than the mean value of all eigenvalues; finally it reconstructs the denoised signal through inverse SVD transformation. Experimental results of comparing the proposed method with traditional wavelet transform and FFT signal denois-ing method show that it has stronger noise robustness and can reserve signal details better, but its implementation speed is somewhat decreased.

  7. A comparison of Monte Carlo dose calculation denoising techniques

    Science.gov (United States)

    El Naqa, I.; Kawrakow, I.; Fippel, M.; Siebers, J. V.; Lindsay, P. E.; Wickerhauser, M. V.; Vicic, M.; Zakarian, K.; Kauffmann, N.; Deasy, J. O.

    2005-03-01

    Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate

  8. A comparison of Monte Carlo dose calculation denoising techniques

    Energy Technology Data Exchange (ETDEWEB)

    Naqa, I El [Washington University, St Louis, MO (United States); Kawrakow, I [National Research Council of Canada, Ottawa, Ontario, Canada (Canada); Fippel, M [Univ Tuebingen, Tuebingen (Germany); Siebers, J V [Virginia Commonwealth University, Richmond, VA (United States); Lindsay, P E [Washington University, St Louis, MO (United States); Wickerhauser, M V [Washington University, St Louis, MO (United States); Vicic, M [Washington University, St Louis, MO (United States); Zakarian, K [Washington University, St Louis, MO (United States); Kauffmann, N [Ecole Polytechnique, Palaiseau (France); Deasy, J O [Washington University, St Louis, MO (United States)

    2005-03-07

    Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards

  9. 遥感影像的自适应小波精细积分降噪方法%Adaptive Wavelet Precise Integration Method on Remote Sensing Image Denoising

    Institute of Scientific and Technical Information of China (English)

    许文宁; 梅树立; 王鹏新; 杨勇

    2011-01-01

    The image denoising variational method possesses higher numerical precision but lower computational efficiency, especially in processing the remote sensing images which is usually larger. To this problem, an adaptive 2-D wavelet interpolation operator was constructed based on quasi-Shannon wavelet function, and then adaptive wavelet precision integration method (PIM) on solving 2-D partial differential equation was proposed by combining with PIM. This proposed method combined the multi-scale property of the wavelet transformation with the higher precision of PIM , which could improve the computational efficiency effectively so that the lager remote sensing image could be processed.%针对遥感影像数据量大、应用精度较高的图像降噪变分法处理时计算效率较低的问题,基于quasi-Shannon小波构造了一种二维自适应小波插值算子,并和精细积分法相结合建立了求解二维偏微分方程自适应小波精细积分方法.利用小波变换的多尺度自适应性和精细积分方法的高精度有效提高了图像降噪变分法的求解效率,从而可实现较大遥感影像的降噪处理.

  10. Non-local means de-noising approach based on dictionary learning%基于字典学习的非局部均值去噪算法

    Institute of Scientific and Technical Information of China (English)

    崔学英; 张权; 桂志国

    2013-01-01

    Concerning the measurement of the similarity of non-local means, a method based on dictionary learning was presented. First, block matching based local pixel grouping was used to eliminate the interference by dissimilar image blocks. Then, the corrupted similar blocks were denoised by dictionary learning. As a further development of classical sparse representation model, the similar patches were unified for joint sparse representation and learning an efficient and compact dictionary by principal component analysis, so that the similar patches relevency could be well preserved. This similarity between the pixels was measured by the Euclidean distance of denoised image blocks, which can well show the similarity of the similar blocks. The experimental results show the modified algorithm has a superior denoising performance than the original one in terms of both Peak Signal-to-Noise Ratio (PSNR) and subjective visual quality. For some images whose structural similarity is large and with rich detail information, their structures and details are well preserved. The robustness of the presented method is superior to the original one.%针对非局部均值中相似度的衡量问题,提出了一种基于字典学习的度量算法.首先利用局部像素群块匹配方法消除不相似的图像块带来的干扰,然后对含有噪声的相似块采用字典学习的方法降噪.与经典的字典学习不同的是,对相似块采用联合稀疏编码的思想,利用主成分分析法学习一个高效紧字典,保留相似块间的相关性信息.采用降噪后的图像块间的欧氏距离计算像素间的相似度,能更好地反映相似块的相似性.实验结果表明,所提出的方法在峰值信噪比和视觉效果方面都优于传统算法,尤其对含有较多细节且结构相似性强的图像,细节和纹理部分的保持效果更好,算法的鲁棒性也优于传统算法.

  11. A denoising algorithm for projection measurements in cone-beam computed tomography.

    Science.gov (United States)

    Karimi, Davood; Ward, Rabab

    2016-02-01

    The ability to reduce the radiation dose in computed tomography (CT) is limited by the excessive quantum noise present in the projection measurements. Sinogram denoising is, therefore, an essential step towards reconstructing high-quality images, especially in low-dose CT. Effective denoising requires accurate modeling of the photon statistics and of the prior knowledge about the characteristics of the projection measurements. This paper proposes an algorithm for denoising low-dose sinograms in cone-beam CT. The proposed algorithm is based on minimizing a cost function that includes a measurement consistency term and two regularizations in terms of the gradient and the Hessian of the sinogram. This choice of the regularization is motivated by the nature of CT projections. We use a split Bregman algorithm to minimize the proposed cost function. We apply the algorithm on simulated and real cone-beam projections and compare the results with another algorithm based on bilateral filtering. Our experiments with simulated and real data demonstrate the effectiveness of the proposed algorithm. Denoising of the projections with the proposed algorithm leads to a significant reduction of the noise in the reconstructed images without oversmoothing the edges or introducing artifacts.

  12. 利用偏微分方程的Tetrolet变换图像去噪%Tetrolet Shrinkage with Partial Differential Equations for Image Denoising

    Institute of Scientific and Technical Information of China (English)

    李财莲; 孙即祥; 康耀红; 李智勇

    2011-01-01

    After tetrolet transform was applied to the noise images, the conventional smooth shrinkage results were further processed by partial differential equations. The simulation results indicated that the method not only remove the noise effectively, but also can obtain higher PSNR ( Peak Signal to Noise Ratio) and better visual effects. Compared with typical tetrolet transform, the denoised images by our method were smoother, and the blocking artifacts were reduced, and which preserved more significant information of original images, such as edges and details.%对图像进行Tetrolet变换后利用偏微分方程对图像进行了质量改善,仿真结果表明,该算法不仅能有效去除噪声,而且可得到更高的峰值信噪比和更好的视觉效果,去噪后图像较光滑,减少了方块效应,更多地保留了图像边缘和细节等局部特征.

  13. Lidar signal de-noising by singular value decomposition

    Science.gov (United States)

    Wang, Huanxue; Liu, Jianguo; Zhang, Tianshu

    2014-11-01

    Signal de-noising remains an important problem in lidar signal processing. This paper presents a de-noising method based on singular value decomposition. Experimental results on lidar simulated signal and real signal show that the proposed algorithm not only improves the signal-to-noise ratio effectively, but also preserves more detail information.

  14. A fast method for video deblurring based on a combination of gradient methods and denoising algorithms in Matlab and C environments

    Science.gov (United States)

    Mirzadeh, Zeynab; Mehri, Razieh; Rabbani, Hossein

    2010-01-01

    In this paper the degraded video with blur and noise is enhanced by using an algorithm based on an iterative procedure. In this algorithm at first we estimate the clean data and blur function using Newton optimization method and then the estimation procedure is improved using appropriate denoising methods. These noise reduction techniques are based on local statistics of clean data and blur function. For estimated blur function we use LPA-ICI (local polynomial approximation - intersection of confidence intervals) method that use an anisotropic window around each point and obtain the enhanced data employing Wiener filter in this local window. Similarly, to improvement the quality of estimated clean video, at first we transform the data to wavelet transform domain and then improve our estimation using maximum a posterior (MAP) estimator and local Laplace prior. This procedure (initial estimation and improvement of estimation by denoising) is iterated and finally the clean video is obtained. The implementation of this algorithm is slow in MATLAB1 environment and so it is not suitable for online applications. However, MATLAB has the capability of running functions written in C. The files which hold the source for these functions are called MEX-Files. The MEX functions allow system-specific APIs to be called to extend MATLAB's abilities. So, in this paper to speed up our algorithm, the written code in MATLAB is sectioned and the elapsed time for each section is measured and slow sections (that use 60% of complete running time) are selected. Then these slow sections are translated to C++ and linked to MATLAB. In fact, the high loads of information in images and processed data in the "for" loops of relevant code, makes MATLAB an unsuitable candidate for writing such programs. The written code for our video deblurring algorithm in MATLAB contains eight "for" loops. These eighth "for" utilize 60% of the total execution time of the entire program and so the runtime should be

  15. 基于双树复小波变换的信号去噪算法%Signal Denoising Algorithm Based on Dual-tree Complex Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    刘文涛; 陈红; 蔡晓霞; 刘俊彤

    2014-01-01

    为了提高接收信号的质量,在一定程度上消除噪声对信号的影响,提出了一种基于双树复小波变换的信号降噪方法。通过双树结构消除了因间隔采样而丢失的有用信息,对每一层的高频分量的实部和虚部分别计算阈值,依据各自的阈值进行滤波处理。实验结果表明:该方法与离散小波变换消噪方法相比具有平移不变性,处理后的波形较平滑,能够较好地保留信号细节信息,而且其去噪性能也优于离散小波变换。%To improve the quality of received signal and eliminate the influence of noise on the signal,a signal denoising algorithm based on Dual-Tree Complex Wavelet Transform(DTCWT)is proposed. Through the double tree structure,the loss of useful information resulting from the sampling is avoided,and then the threshold of real part and imaginary part of high frequency component for each floor is calculated separately,and filter processing according to their respective threshold. Simulation results show that this algorithm has the translation invariance compared with the Discrete Wavelet Transform (DWT)denoising algorithm,and the waveform is not only relatively smooth and keeps the details of the signal better after processing,but also denoising performance is superior to the DWT.

  16. 小波方向子带偏微分方程遥感图像去噪%Remote sensing image de-noising on partial differential equation in wavelet directional subband

    Institute of Scientific and Technical Information of China (English)

    王相海; 李放; 王爽

    2012-01-01

    The noise analysis and elimination in remote sensing images has attracted considerable attention, and has become an important research field for remote sensing image processing. In this paper, we propose a wavelet threshold method to de-noise the Gaussian noise in remote sensing image to make the edge fuzzy causing by over the existence of the " strangulation" of the wavelet coefficients, as well as P-M model usually tends to make the image gray sub-conatant, resulting the so-called " massive" effect problem, This paper proposes a new remote sensing image denoising model based on wavelet partial differential equations (PDE) to address the above mentioned issue. This model decomposes remote sensing images by wavelets and maintain the low-frequency subband information. Only with nnise and the edge's high-frequency sub-band based on sub-band directional characteristics of the nonlinear anisotropic diffusion, this model can remove Gaussian noise well and, at the same time, can also protect the edge features and details of remote sensing image, and avoids to appear the piecewise constant phenomenon. Experimental results show that our model gains 1 ~ 2dB higher PSNR than the class of zero-tree based on Bayes model threshold and P-M model.%针对小波阈值法在去除遥感图像高斯噪声时,所存在的由于过度“扼杀”小波系数而引起的模糊边缘问题,以及P-M模型通常会使图像的灰度趋于分段常量而产生所谓的“块状”效应问题.提出小波域偏微分方程(PDE)遥感图像去噪模型,该模型通过对遥感图像进行小波分解,保持低频子带信息,而只对含有噪声、图像边缘的高频子带进行基于子带方向特性的非线性异性扩散,使模型在有效去除高斯噪声的同时,能够很好地保护遥感图像中的边缘特征和细节纹理信息,避免了去噪后的结果图像出现分段常量现象.实验结果表明,对于相同的遥感图像高斯噪声,基于所提

  17. A Variational Framework for Exemplar-Based Image Inpainting

    Science.gov (United States)

    2010-04-01

    continuing and imposing this model inside the inpainting domain, usually by means of a partial differential equation (PDE). Such PDE can be derived from...STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Non-local methods for image denoising and...Facciolo · Vicent Caselles · Guillermo Sapiro Abstract Non-local methods for image denoising and inpainting have gained considerable attention in recent

  18. Improved EEMD Denoising Method Based on Singular Value Decomposition for the Chaotic Signal

    Directory of Open Access Journals (Sweden)

    Xiulei Wei

    2016-01-01

    Full Text Available Chaotic data analysis is important in many areas of science and engineering. However, the chaotic signals are inevitably contaminated by complicated noise in the collection process which greatly interferes with the analysis of chaos identification. The chaotic vibration is extremely nonlinear and has a broad range of frequencies; linear filtering methods are not effective for chaotic signal noise reduction. Then an improved ensemble empirical mode decomposition (EEMD based on singular value decomposition (SVD and Savitzky-Golay (SG filtering method was proposed. Firstly, the noise energy of first level intrinsic mode function (IMF was estimated by “3σ” criterion, and then SVD was used to extract the signal details from first IMF, and the singular value was selected to reconstruct the IMF according to noise energy of the first IMF. Secondly, the remaining IMFs are divided into high frequency and low frequency components based on consecutive mean square error (CMSE, and the useful signals of high frequency components and low frequency components are extracted based on SVD and SG filtering method, respectively. The superiority of the proposed method is demonstrated with simulated signal, two-degree-of-freedom chaotic vibration signals, and the experimental signals based on double potential well theory.

  19. Denoising and Back Ground Clutter of Video Sequence using Adaptive Gaussian Mixture Model Based Segmentation for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    Shanmugapriya. K

    2014-01-01

    Full Text Available The human action recognition system first gathers images by simply querying the name of the action on a web image search engine like Google or Yahoo. Based on the assumption that the set of retrieved images contains relevant images of the queried action, we construct a dataset of action images in an incremental manner. This yields a large image set, which includes images of actions taken from multiple viewpoints in a range of environments, performed by people who have varying body proportions and different clothing. The images mostly present the “key poses” since these images try to convey the action with a single pose. In existing system to support this they first used an incremental image retrieval procedure to collect and clean up the necessary training set for building the human pose classifiers. There are challenges that come at the expense of this broad and representative data. First, the retrieved images are very noisy, since the Web is very diverse. Second, detecting and estimating the pose of humans in still images is more difficult than in videos, partly due to the background clutter and the lack of a foreground mask. In videos, foreground segmentation can exploit motion cues to great benefit. In still images, the only cue at hand is the appearance information and therefore, our model must address various challenges associated with different forms of appearance. Therefore for robust separation, in proposed work a segmentation algorithm based on Gaussian Mixture Models is proposed which is adaptive to light illuminations, shadow and white balance is proposed here. This segmentation algorithm processes the video with or without noise and sets up adaptive background models based on the characteristics also this method is a very effective technique for background modeling which classifies the pixels of a video frame either background or foreground based on probability distribution.

  20. Focal artifact removal from ongoing EEG--a hybrid approach based on spatially-constrained ICA and wavelet de-noising.

    Science.gov (United States)

    Akhtar, Muhammad Tahir; James, Christopher J

    2009-01-01

    Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. It has already been demonstrated that independent component analysis (ICA) can be an effective and applicable method for EEG de-noising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ the concept of spatially-constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as all ICs are not identified in the usual manner due to the square mixing assumption. Simulation results demonstrate the effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.

  1. Using Normal Inverse Gaussian Model for Image Denoising in NSCT Domain%基于正态逆高斯模型的非下采样Contourlet变换图像去噪

    Institute of Scientific and Technical Information of China (English)

    贾建; 陈莉

    2011-01-01

    提出一种基于正态逆高斯先验模型的非下采样Contourlet变换图像去噪算法.在非下采样Contourlet变换域中,以正态逆高斯模型为先验模型,对图像分解系数的稀疏分布统计建模,估计每个子带内的模型参数,在贝叶斯最大后验概率估计准则下推导出与正态逆高斯模型相应的阈值函数表达式,以此对图像进行去噪处理.对于被加性高斯白噪声污染的图像,实验结果表明该去噪算法能有效地去除图像中的高斯白噪声,提高图像的峰值信噪比值,在边缘特征方面保持了良好的视觉效果.%A novel non-subsampled Contourlet transform denoising scheme based on the normal inverse Gaussian prior (NIG) and Bayesian estimation has been proposed. Normal inverse Gaussian model is used to describe the distributions of the image coefficients of each subband in non-subsampled Contourlet transform domain, corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance.The simulation results indicate that the proposed method can remove Gaussian white noise effectively, improve the peak signal-to-noise ratio of the image, and keep better visual result in edges information reservation as well.

  2. Lidar signal de-noising based on wavelet trimmed thresholding technique

    Institute of Scientific and Technical Information of China (English)

    Haitao Fang(方海涛); Deshuang Huang(黄德双)

    2004-01-01

    Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.

  3. Method of Infrared Image Enhancement Based on Stationary Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    QI Fei; LI Yan-jun; ZHANG Ke

    2008-01-01

    Aiming at the problem, i.e. infrared images own the characters of bad contrast ratio and fuzzy edges, a method to enhance the contrast of infrared image is given, which is based on stationary wavelet transform. After making stationary wavelet transform to an infrared image, denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity. For the approximation coefficient matrix with low noisy intensity, enhancement is done by the proposed method based on histogram. The enhanced image can be got by wavelet coefficient reconstruction. Furthermore, an evaluation criterion of enhancement performance is introduced. The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively. At the same time, its amount of calculation is small and operation speed is fast.

  4. Real-time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware

    Institute of Scientific and Technical Information of China (English)

    Yang Su; Zhi-Jie Xu; Xiang-Qian Jiang

    2009-01-01

    The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of thc Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

  5. 利用均匀离散曲波域LCHMM的图像降噪算法%Image denoising algorithm using Local Contextual Hidden Markov Model in uniform discrete curvelet domain

    Institute of Scientific and Technical Information of China (English)

    吴俊政; 严卫东; 边辉; 倪维平

    2012-01-01

    Using the Local Contextual Hidden Markov Model(LCHMM) in uniform discrete curvelet domain, an image denoising algorithm is proposed. After introducing the characteristics of the new transform, the statistical distribution rules of it are analyzed, which shows that the hidden markov model is suited to model the new transform's coefficients. The estimative coefficients of denoised image can be abtained by the model' s parameters, which are captured through expectation maximization training method. The proposed algorithm is applied to denoising the optical image and high resolution SAR image respectively. Compared with the LCHMMs in wavelet and contourlet domain, the experimental results show that the proposed algorithm can reduce noise effectively with well edge-preserving ability.%提出了一种在均匀离散曲波域中利用局部上下文隐马尔可夫模型进行建模的图像降噪算法.介绍均匀离散曲波变换的特点,分析其系数的统计分布规律,表明适合用隐马尔可夫模型对其进行建模.通过期望最大化训练获取模型的参数,利用参数得到降噪图像的系数估计.分别对光学图像和高分辨率的SAR图像进行了降噪实验,与小波域、轮廓波域的局部上下文隐马尔可夫模型等降噪方法进行比较,结果表明,提出的算法能够有效地去除噪声,具有较强的边缘保持能力.

  6. Noise reduction of cDNA microarray images using complex wavelets.

    Science.gov (United States)

    Howlader, Tamanna; Chaubey, Yogendra P

    2010-08-01

    Noise reduction is an essential step of cDNA microarray image analysis for obtaining better-quality gene expression measurements. Wavelet-based denoising methods have shown significant success in traditional image processing. The complex wavelet transform (CWT) is preferred to the classical discrete wavelet transform for denoising of microarray images due to its improved directional selectivity for better representation of the circular edges of spots and near shift-invariance property. Existing CWT-based denoising methods are not efficient for microarray image processing because they fail to take into account the signal as well as noise correlations that exist between red and green channel images. In this paper, two bivariate estimators are developed for the CWT-based denoising of microarray images using the standard maximum a posteriori and linear minimum mean squared error estimation criteria. The proposed denoising methods are capable of taking into account both the interchannel signal and noise correlations. Significance of the proposed denoising methods is assessed by examining the effect of noise reduction on the estimation of the log-intensity ratio. Extensive experimentations are carried out to show that the proposed methods provide better noise reduction of microarray images leading to more accurate estimation of the log-intensity ratios as compared to the other CWT-based denoising methods.

  7. Denoising of ECG signal during spaceflight using singular value decomposition

    Science.gov (United States)

    Li, Zhuo; Wang, Li

    2009-12-01

    The Singular Value Decomposition (SVD) method is introduced to denoise the ECG signal during spaceflight. The theory base of SVD method is given briefly. The denoising process of the strategy is presented combining a segment of real ECG signal. We improve the algorithm of calculating Singular Value Ratio (SVR) spectrum, and propose a constructive approach of analysis characteristic patterns. We reproduce the ECG signal very well and compress the noise effectively. The SVD method is proved to be suitable for denoising the ECG signal.

  8. Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman

    Directory of Open Access Journals (Sweden)

    Pascal Getreuer

    2012-05-01

    Full Text Available Denoising is the problem of removing noise from an image. The most commonly studied case is with additive white Gaussian noise (AWGN, where the observed noisy image f is related to the underlying true image u by f=u+η and η is at each point in space independently and identically distributed as a zero-mean Gaussian random variable. Total variation (TV regularization is a technique that was originally developed for AWGN image denoising by Rudin, Osher, and Fatemi. The TV regularization technique has since been applied to a multitude of other imaging problems, see for example Chan and Shen's book. We focus here on the split Bregman algorithm of Goldstein and Osher for TV-regularized denoising.

  9. A New Image Denoising Algorithms of Fourth-order Partial Differential Equation%一种新的四阶偏微分方程的图像降噪算法

    Institute of Scientific and Technical Information of China (English)

    杨薇; 王楠

    2012-01-01

    提出一种基于四阶偏微分方程的图像降噪算法——耦合梯度保真项的四阶偏微分方程的图像降噪算法,实验结果验证了算法的有效性.%This paper advances the fourth-order partial differential equation coupling gradient fidelity term image denoising algorithms.The experiment result shows the efficiency of the algorithm.

  10. Speech signal denoising with wavelet-transforms and the mean opinion score characterizing the filtering quality

    Science.gov (United States)

    Yaseen, Alauldeen S.; Pavlov, Alexey N.; Hramov, Alexander E.

    2016-03-01

    Speech signal processing is widely used to reduce noise impact in acquired data. During the last decades, wavelet-based filtering techniques are often applied in communication systems due to their advantages in signal denoising as compared with Fourier-based methods. In this study we consider applications of a 1-D double density complex wavelet transform (1D-DDCWT) and compare the results with the standard 1-D discrete wavelet-transform (1DDWT). The performances of the considered techniques are compared using the mean opinion score (MOS) being the primary metric for the quality of the processed signals. A two-dimensional extension of this approach can be used for effective image denoising.

  11. 多孔小波和非下采样滤波器组去除遥感图像的多种噪声%Remote Sensing Image Denoising by àtrous Wavelet and Nonsubsampled Directional Filter Bank

    Institute of Scientific and Technical Information of China (English)

    徐华楠; 刘哲; 刘灿

    2012-01-01

    This paper proposed a method of denoising the remote sensing image based on àtrous-nonsubsampled contourlet transform.The method uses the àtrous wavelet—an undecimated discrete wavelet transform algorithm to decompose the image into two parts possessing approximate part and detail parts,which are the same size as the original image.Then the nonsubsampled directional filter bank is employed to decompose the detail parts into directional subbands.The different kinds of noises of the remote sensing image can be decomposed into the wavelet coefficients in different scale and directions,with which the best method can be chosen based on the characteristics of the different noises.It is more scientific and more effective than just using one method for all kinds of noises in the past.It is proved that the method proposed in the paper is more useful in removing the noise of the image,reserving richer fine textures and edge information than other traditional filtering methods.%结合àtrous小波变换和非下采样轮廓波变换的优点,提出一种基于àtrous-非下采样轮廓波变换的遥感图像去噪方法.该方法用非抽取离散小波变换的àtrous算法对图像进行多尺度分解,然后用非下采样的多方向滤波器组对得到的细节分量进行多方向分解.对含有多种噪声的遥感图像,àtrous-非下采样轮廓波变换将图像中不同种类的噪声分解到不同的小波系数分量中,使得可以根据噪声特性选择最合适的去噪方法,比用一种方法去除所有类型的噪声更科学且去噪效果更好.

  12. Denoising and enhancement in intravascular ultrasound images via multiscale analysis%基于多尺度分析的血管内超声图像去噪和增强

    Institute of Scientific and Technical Information of China (English)

    李虹; 王惠南; 章哓国

    2008-01-01

    提出基于二进小波变换的血管内超声图像血液斑点噪声抑制和对比度增强算法.血液红细胞散射引起的斑点噪声属于乘性噪声,在对数域进行二进小波变换后,结合软阈值滤波法和硬阈值滤波法对不同尺度的小波系数进行萎缩处理,并提出了一种局部阈值估计方法.同时采用了基于多尺度边缘表示的,利用小波系数极值拉伸和Hermite多项式插值实现的快速增强算法.实验结果表明,与现有单独进行去噪处理的方法相比,该方法在抑制血液斑点噪声的同时增强了图像对比度,具有更好的实用性.%An algorithm based on dyadic wavelet transform for speckle reduction and contrast enhancement in intravascular ultrasound images was presented. Wavelet shrinkage techniques which combined soft and hard thresholding were applied to coefficients of logarithmically transformed images since scattering from red blood cells (blood speckle noise) was multiplicative noise and a method to estimate local threshold was proposed. In addition, a fast contrast enhancement algorithm based on multi-scale edges representation of images through stretching the local extrema and interpolating them with Hermite interpolation polynomials was carried out. Experiments with clinical images showed that this algorithm was capable of not only reducing the speckle noise of blood but also enhancing features of diagnostic importance of intravascular ultrasound images and produced superior results qualitatively when compared to results obtained from existing denoising methods alone.

  13. Wavelet-Based Diffusion Approach for DTI Image Restoration

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiang-fen; CHEN Wu-fan; TIAN Wei-feng; YE Hong

    2008-01-01

    The Rician noise introduced into the diffusion tensor images (DTIs) can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet-based diffusion method to denoise multichannel typed diffusion weighted (DW) images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio (PSNR) and signal-to-mean squared error ratio (SMSE) metrics are adopted. Based on the synthetic and real data, we calculated the apparent diffusion coefficient (ADC) and tracked the fibers. We made comparisons between the presented model,the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.

  14. Denoising time-domain induced polarisation data using wavelet techniques

    Science.gov (United States)

    Deo, Ravin N.; Cull, James P.

    2016-05-01

    Time-domain induced polarisation (TDIP) methods are routinely used for near-surface evaluations in quasi-urban environments harbouring networks of buried civil infrastructure. A conventional technique for improving signal to noise ratio in such environments is by using analogue or digital low-pass filtering followed by stacking and rectification. However, this induces large distortions in the processed data. In this study, we have conducted the first application of wavelet based denoising techniques for processing raw TDIP data. Our investigation included laboratory and field measurements to better understand the advantages and limitations of this technique. It was found that distortions arising from conventional filtering can be significantly avoided with the use of wavelet based denoising techniques. With recent advances in full-waveform acquisition and analysis, incorporation of wavelet denoising techniques can further enhance surveying capabilities. In this work, we present the rationale for utilising wavelet denoising methods and discuss some important implications, which can positively influence TDIP methods.

  15. A New Method of Signal De-Noising by Multi-Resolution Analysis Based on Discrete Element%基于离散元的多分辨率信号去噪新方法

    Institute of Scientific and Technical Information of China (English)

    张江源; 林福泳

    2013-01-01

    By constructing a discrete base, multi-resolution analysis methods are applied to denoise the noise signal. The proposed method of discrete base can be explicitly represented, and has symmetric characteristics. The calculation is greatly reduced through the cycle matrix inverse matrix method. Comparing different denoising methods, the signal de-noising effect is assessed from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE). Experimental results indicate that this method shows good characteristics in signal denoising aspect relative to wavelet analysis method. Denoising effect is obvious, and can achieve good signal-to-noise ratio and mean square error when discrete base coefficient is near 0. 75.%通过构造离散基,应用多分辨率分析的方法,对噪声信号进行去噪处理.所提出方法的离散基能够显式表示,且具有对称性等特点,通过循环矩阵求逆矩阵的方法,可以使计算量大大降低.对比不同的去噪方法,并分别从信噪比(SNR)和均方误差(MSE)两个方面对信号去噪效果进行评估.实验结果表明:相对小波分析方法而言,该方法在信号去噪方面表现出较好的特性,去噪效果明显,离散基系数在0.75附近达到较好的信噪比及均方误差.

  16. The application of Contourlet Transform in Image Denoising%Contourlet变换在图像降噪中的应用

    Institute of Scientific and Technical Information of China (English)

    蔡文涛

    2012-01-01

    Contourlet是一个真正的二维图像表示方法,变换由两层滤波器组成:拉普拉斯变换和多方向分解。Contourlet变换可以实现多分辨率和多向分解灵活掌握图像,与小波变换相比较,Contourlet变换首先提出将离散域数字化,然后扩展到连续域分析其属性,它能够更方便地将图像呈现出来。%Contourlet is a true two-dimensional image representation, its transform consists of two layers of filter: Laplace transform and multi-directional decomposition. Contourlet transform can achieve multi-resolution and muhi-image decomposition to flexibly grip the image. Compared with wavelet transform, Contourlet transform firstly proposes the digitized discrete domain, and then extends to the continuous domain to analysis its properties, it can be more convenient to present the image.

  17. Geometric properties of solutions to the total variation denoising problem

    Science.gov (United States)

    Chambolle, Antonin; Duval, Vincent; Peyré, Gabriel; Poon, Clarice

    2017-01-01

    This article studies the denoising performance of total variation (TV) image regularization. More precisely, we study geometrical properties of the solution to the so-called Rudin-Osher-Fatemi total variation denoising method. The first contribution of this paper is a precise mathematical definition of the ‘extended support’ (associated to the noise-free image) of TV denoising. It is intuitively the region which is unstable and will suffer from the staircasing effect. We highlight in several practical cases, such as the indicator of convex sets, that this region can be determined explicitly. Our second and main contribution is a proof that the TV denoising method indeed restores an image which is exactly constant outside a small tube surrounding the extended support. The radius of this tube shrinks toward zero as the noise level vanishes, and we are able to determine, in some cases, an upper bound on the convergence rate. For indicators of so-called ‘calibrable’ sets (such as disks or properly eroded squares), this extended support matches the edges, so that discontinuities produced by TV denoising cluster tightly around the edges. In contrast, for indicators of more general shapes or for complicated images, this extended support can be larger. Beside these main results, our paper also proves several intermediate results about fine properties of TV regularization, in particular for indicators of calibrable and convex sets, which are of independent interest.

  18. Compressive Hyperspectral Imaging and Anomaly Detection

    Science.gov (United States)

    2010-02-01

    Examples include the discrete cosine basis and various wavelets based bases. They have been thoroughly studied and widely considered in applications...the desired jointly sparse a"s, one shall adjust a and b. 4.4 Hyperspectral Image Reconstruction and Denoising We apply the model x* = Da’ + e! to...iteration for compressive sensing and sparse denoising ,’" Communications in Mathematical Sciences , 2008. W. Yin, "Analysis and generalizations of

  19. Frequency domain reciprocal—Gaussian cascade low-pass filtering denoising method of image%图像的频域倒数—高斯级联低通滤波去噪方法

    Institute of Scientific and Technical Information of China (English)

    王杰; 毛玉泉; 李思佳; 吴崇虎

    2012-01-01

    针对图像频域滤波中细节信息丢失的问题,提出了一种频域倒数—高斯级联低通滤波去噪方法.该方法在频域利用倒数快速收敛的性质并结合高斯低通滤波器,实现了图像的联合滤波去噪.其在有效滤除高频噪声的同时更大限度地保留了图像的细节分量,进而使处理后的图像具有较高的对比度,对于去除噪声、提高图像质量有显著的效果.仿真结果表明,在相同的有效滤波面积基础上,与传统低通滤波器、倒数一理想级联低通滤波器、倒数—巴特沃斯级联低通滤波器相比,该方法的去噪效果最佳.%To reduce the loss of details in the image frequency domain filtering, this paper proposed a new frequency method that reciprocal—Gaussian cascade low-pass filter. The method utilized the nature of reciprocal fast convergence and combined with Gaussian low-pass filter to achieve the joint filter of the image, it had significant impression on improving image quality by keeping a greater measure of image detail component while filtering the high-frequency noise, it processed the after image having higher contrast. Simulation shows that, comparing with traditional low-pass filter, reciprocal—ideal cascade low-pass filter and reciprocal—Butterworth cascade low-pass filter based on the same effective filter area, the proposed method is the best on denoising effect.

  20. Wavelets Applied to CMB Maps a Multiresolution Analysis for Denoising

    CERN Document Server

    Sanz, J L; Cayon, L; Martínez-González, E; Barriero, R B; Toffolatti, L

    1999-01-01

    Analysis and denoising of Cosmic Microwave Background (CMB) maps are performed using wavelet multiresolution techniques. The method is tested on $12^{\\circ}.8\\times 12^{\\circ}.8$ maps with resolution resembling the experimental one expected for future high resolution space observations. Semianalytic formulae of the variance of wavelet coefficients are given for the Haar and Mexican Hat wavelet bases. Results are presented for the standard Cold Dark Matter (CDM) model. Denoising of simulated maps is carried out by removal of wavelet coefficients dominated by instrumental noise. CMB maps with a signal-to-noise, $S/N \\sim 1$, are denoised with an error improvement factor between 3 and 5. Moreover we have also tested how well the CMB temperature power spectrum is recovered after denoising. We are able to reconstruct the $C_{\\ell}$'s up to $l\\sim 1500$ with errors always below $20% $ in cases with $S/N \\ge 1$.

  1. Multi-Scale Patch-Based Image Restoration.

    Science.gov (United States)

    Papyan, Vardan; Elad, Michael

    2016-01-01

    Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them separately, and then merge the results by a plain averaging. This concept has been demonstrated to be highly effective, leading often times to the state-of-the-art results in denoising, inpainting, deblurring, segmentation, and other applications. While the above is indeed effective, this approach has one major flaw: the prior is imposed on intermediate (patch) results, rather than on the final outcome, and this is typically manifested by visual artifacts. The expected patch log likelihood (EPLL) method by Zoran and Weiss was conceived for addressing this very problem. Their algorithm imposes the prior on the patches of the final image, which in turn leads to an iterative restoration of diminishing effect. In this paper, we propose to further extend and improve the EPLL by considering a multi-scale prior. Our algorithm imposes the very same prior on different scale patches extracted from the target image. While all the treated patches are of the same size, their footprint in the destination image varies due to subsampling. Our scheme comes to alleviate another shortcoming existing in patch-based restoration algorithms--the fact that a local (patch-based) prior is serving as a model for a global stochastic phenomenon. We motivate the use of the multi-scale EPLL by restricting ourselves to the simple Gaussian case, comparing the aforementioned algorithms and showing a clear advantage to the proposed method. We then demonstrate our algorithm in the context of image denoising, deblurring, and super-resolution, showing an improvement in performance both visually and quantitatively.

  2. An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms.

    Science.gov (United States)

    Fernández, José Jesús; Li, Sam

    2003-01-01

    Cryo-electron tomography is an imaging technique with an unique potential for visualizing large complex biological specimens. It ensures preservation of the biological material but the resulting cryotomograms are extremely noisy. Sophisticated denoising techniques are thus essential for allowing the visualization and interpretation of the information contained in the cryotomograms. Here a software tool based on anisotropic nonlinear diffusion is described for filtering cryotomograms. The approach reduces local noise and meanwhile enhances both curvilinear and planar structures. In the program a novel solution of the partial differential equation has been implemented, which allows a reliable estimation of derivatives and, furthermore, reduces computation time and memory requirements. Several criteria have been included to automatically select the optimal stopping time. The behaviour of the denoising approach is tested for visualizing filamentous structures in cryotomograms.

  3. Compression of Ultrasonic NDT Image by Wavelet Based Local Quantization

    Science.gov (United States)

    Cheng, W.; Li, L. Q.; Tsukada, K.; Hanasaki, K.

    2004-02-01

    Compression on ultrasonic image that is always corrupted by noise will cause `over-smoothness' or much distortion. To solve this problem to meet the need of real time inspection and tele-inspection, a compression method based on Discrete Wavelet Transform (DWT) that can also suppress the noise without losing much flaw-relevant information, is presented in this work. Exploiting the multi-resolution and interscale correlation property of DWT, a simple way named DWCs classification, is introduced first to classify detail wavelet coefficients (DWCs) as dominated by noise, signal or bi-effected. A better denoising can be realized by selective thresholding DWCs. While in `Local quantization', different quantization strategies are applied to the DWCs according to their classification and the local image property. It allocates the bit rate more efficiently to the DWCs thus achieve a higher compression rate. Meanwhile, the decompressed image shows the effects of noise suppressed and flaw characters preserved.

  4. Wavelet De-noising of Speech Using Singular Spectrum Analysis for Decomposition Level Selection

    Institute of Scientific and Technical Information of China (English)

    CAI Tie; ZHU Jie

    2007-01-01

    The problem of speech enhancement using threshold de-noising in wavelet domain was considered. The appropriate decomposition level is another key factor pertinent to de-noising performance. This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise. The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech. The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.

  5. HARDI DATA DENOISING USING VECTORIAL TOTAL VARIATION AND LOGARITHMIC BARRIER

    Science.gov (United States)

    Kim, Yunho; Thompson, Paul M.; Vese, Luminita A.

    2010-01-01

    In this work, we wish to denoise HARDI (High Angular Resolution Diffusion Imaging) data arising in medical brain imaging. Diffusion imaging is a relatively new and powerful method to measure the three-dimensional profile of water diffusion at each point in the brain. These images can be used to reconstruct fiber directions and pathways in the living brain, providing detailed maps of fiber integrity and connectivity. HARDI data is a powerful new extension of diffusion imaging, which goes beyond the diffusion tensor imaging (DTI) model: mathematically, intensity data is given at every voxel and at any direction on the sphere. Unfortunately, HARDI data is usually highly contaminated with noise, depending on the b-value which is a tuning parameter pre-selected to collect the data. Larger b-values help to collect more accurate information in terms of measuring diffusivity, but more noise is generated by many factors as well. So large b-values are preferred, if we can satisfactorily reduce the noise without losing the data structure. Here we propose two variational methods to denoise HARDI data. The first one directly denoises the collected data S, while the second one denoises the so-called sADC (spherical Apparent Diffusion Coefficient), a field of radial functions derived from the data. These two quantities are related by an equation of the form S = SSexp (−b · sADC) (in the noise-free case). By applying these two different models, we will be able to determine which quantity will most accurately preserve data structure after denoising. The theoretical analysis of the proposed models is presented, together with experimental results and comparisons for denoising synthetic and real HARDI data. PMID:20802839

  6. A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising

    Directory of Open Access Journals (Sweden)

    Can He

    2015-01-01

    Full Text Available Due to simple calculation and good denoising effect, wavelet threshold denoising method has been widely used in signal denoising. In this method, the threshold is an important parameter that affects the denoising effect. In order to improve the denoising effect of the existing methods, a new threshold considering interscale correlation is presented. Firstly, a new correlation index is proposed based on the propagation characteristics of the wavelet coefficients. Then, a threshold determination strategy is obtained using the new index. At the end of the paper, a simulation experiment is given to verify the effectiveness of the proposed method. In the experiment, four benchmark signals are used as test signals. Simulation results show that the proposed method can achieve a good denoising effect under various signal types, noise intensities, and thresholding functions.

  7. 基于非局部TV正则化的波原子去噪算法%Denoising Algorithm with Wave Atoms Based on Nonlocal TV Regularization

    Institute of Scientific and Technical Information of China (English)

    吴玉莲; 冯象初

    2011-01-01

    结合新的多尺度几何分析工具波原子和非局部TV正则化提出了一种新的纹理图像去噪模型.该模型充分利用了波原子对振荡纹理图像的稀疏表示和非局部TV能较好地处理纹理图像的优点,使得新方法处理后的纹理图像避免了伪吉布斯振荡现象.实验结果表明,新方法的信噪比有一定提高,在保持图像的细节方面与单纯的波原子阈值和非局部TV比较也有明显的改善,取得了比较好的视觉效果.%A novel denoising model for texture images was proposed, which combines the new multiscale geometric analysis tool-wave atoms and nonlocal total variation regularization scheme. This model well considers the advantages:wave atoms which has the ability of sparse representation of the oscillatory texture images and nonlocal TV version has the ability to handle better textures. Numerical experiments show that the proposed model significantly improves the SNR, performance of preserving details is much better than both only wave atoms threshold and nonlocal TV, therefore together with better visual effects.

  8. An Improved Denoising Model Based on Partial Differential Equations%一种改进的基于PDE的图像去噪模型

    Institute of Scientific and Technical Information of China (English)

    刘晓娜; 刘朝霞

    2011-01-01

    In this paper, on the basis of the previous work of Perona and Malik and Catte, etc. , we propose the modified model of image denoising, do the model's numerical simulation. The experimental results show that the model can not only remove the noise effectively, but also to enhance and maintain the edge, and make the processed image sharpness. And contrast are greatly increased, looks also more really and naturally.%本文在Perona和Malik及Catte等前人的工作基础上提出了一种新的图像去噪模型,并做了数值仿真,实验结果证明该模型不但能有效地去除噪声,而且能增强边缘并保持边缘位置,使处理后的图像清晰度和对比度都有大大增加,看上去也比较真实自然.

  9. Denoising of gravitational wave signals via dictionary learning algorithms

    Science.gov (United States)

    Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

    2016-12-01

    Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

  10. Fast DOA estimation using wavelet denoising on MIMO fading channel

    CERN Document Server

    Meenakshi, A V; Kayalvizhi, R; Asha, S

    2011-01-01

    This paper presents a tool for the analysis, and simulation of direction-of-arrival (DOA) estimation in wireless mobile communication systems over the fading channel. It reviews two methods of Direction of arrival (DOA) estimation algorithm. The standard Multiple Signal Classification (MUSIC) can be obtained from the subspace based methods. In improved MUSIC procedure called Cyclic MUSIC, it can automatically classify the signals as desired and undesired based on the known spectral correlation property and estimate only the desired signal's DOA. In this paper, the DOA estimation algorithm using the de-noising pre-processing based on time-frequency conversion analysis was proposed, and the performances were analyzed. This is focused on the improvement of DOA estimation at a lower SNR and interference environment. This paper provides a fairly complete image of the performance and statistical efficiency of each of above two methods with QPSK signal.

  11. Total Variation Denoising and Support Localization of the Gradient

    Science.gov (United States)

    Chambolle, A.; Duval, V.; Peyré, G.; Poon, C.

    2016-10-01

    This paper describes the geometrical properties of the solutions to the total variation denoising method. A folklore statement is that this method is able to restore sharp edges, but at the same time, might introduce some staircasing (i.e. “fake” edges) in flat areas. Quite surprisingly, put aside numerical evidences, almost no theoretical result are available to backup these claims. The first contribution of this paper is a precise mathematical definition of the “extended support” (associated to the noise-free image) of TV denoising. This is intuitively the region which is unstable and will suffer from the staircasing effect. Our main result shows that the TV denoising method indeed restores a piece-wise constant image outside a small tube surrounding the extended support. Furthermore, the radius of this tube shrinks toward zero as the noise level vanishes and in some cases, an upper bound on the convergence rate is given.

  12. De-noising method based on sparse representation and its application invibration control%基于稀疏表征的降噪方法及其在振动激励控制中的应用

    Institute of Scientific and Technical Information of China (English)

    孟利波; 秦毅; 合烨; 郭磊

    2016-01-01

    Aimed at the fact that the control accuracy declines due to the noise effect on the vibration control ,a de-noising method based on sparse representation is put forward to improve the control signal . To obtain a better noise -reduction result , the basis pursuit denoising method based on split variable augmented Lagrangian shrinkage algorithm ( SALSA ) is improved by using kurtosis as measurement index.Then,a method for obtaining the optimized Lagrange multiplier parameter is proposed ,which can improve the denoising performance of basis pursuit denoising .Finally the de-noising signal is fed back into the front controller in order to obtain the expected control effect .The simulation result validates the effectiveness of basis pursuit denoising based on SALSA .Finally,the proposed method is applied to the vibration control of a water water -lubricated bearing test , and the results show that this method can effectively remove the noises with different intensity from the sampled torque signals ,thus it can be well used in the feedback element of the vibration control .%针对振动控制中因混有噪声而导致控制精度降低,影响振动控制研究效果的现实问题,提出基于稀疏表征的降噪方法,并通过引入峭度作为测度指标改进基于分离变量的增广拉格朗日收敛算法( SALSA )的基追踪降噪方法。然后,提出获取优化的拉格朗日乘数的方法,从而使得基追踪降噪可以获得更佳的降噪性能。最后将降噪信号反馈给前端控制器,以达到预期的控制目的。通过仿真实例,验证基于SALSA的基追踪降噪方法的有效性。结果表明:将该方法用于水润滑轴承实验的振动控制,能够有效地去除所测力矩信号中不同强度的高斯白噪声,因而可以较好地应用在振动控制中的反馈环节。

  13. Dictionary Based Image Segmentation

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2015-01-01

    We propose a method for weakly supervised segmentation of natural images, which may contain both textured or non-textured regions. Our texture representation is based on a dictionary of image patches. To divide an image into separated regions with similar texture we use an implicit level sets...... in an efficient implementation of our segmentation method. We experimentally validated our approach on a number of natural as well as composed images....

  14. Airborne Gravity Data Denoising Based on Empirical Mode Decomposition: A Case Study for SGA-WZ Greenland Test Data

    Directory of Open Access Journals (Sweden)

    Lei Zhao

    2015-10-01

    Full Text Available Surveying the Earth’s gravity field refers to an important domain of Geodesy, involving deep connections with Earth Sciences and Geo-information. Airborne gravimetry is an effective tool for collecting gravity data with mGal accuracy and a spatial resolution of several kilometers. The main obstacle of airborne gravimetry is extracting gravity disturbance from the extremely low signal to noise ratio measuring data. In general, the power of noise concentrates on the higher frequency of measuring data, and a low pass filter can be used to eliminate it. However, the noise could distribute in a broad range of frequency while low pass filter cannot deal with it in pass band of the low pass filter. In order to improve the accuracy of the airborne gravimetry, Empirical Mode Decomposition (EMD is employed to denoise the measuring data of two primary repeated flights of the strapdown airborne gravimetry system SGA-WZ carried out in Greenland. Comparing to the solutions of using finite impulse response filter (FIR, the new results are improved by 40% and 10% of root mean square (RMS of internal consistency and external accuracy, respectively.

  15. The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2014-09-01

    Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

  16. μ-SVD降噪算法及其在齿轮故障诊断中的应用%μ-SVD Based Denoising Method and Its Application to Gear Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    曾鸣; 杨宇; 郑近德; 程军圣

    2015-01-01

    为了提取机械设备被强背景噪声淹没的故障特征,采用一种具有通用意义的基于奇异值分解(Singular value decomposition, SVD)的子空间降噪算法对信号进行处理,即μ-SVD降噪算法。传统的SVD降噪算法是μ-SVD降噪算法中拉格朗日乘子μ=0时的一种特殊情况。μ-SVD降噪算法包含滤值因子,能够抑制以噪声贡献占主导的奇异值对降噪后信号的信息贡献量。μ-SVD 降噪算法涉及延迟时间、嵌入维数、降噪阶次、噪声功率和拉格朗日乘子等5个参数。讨论了μ-SVD降噪算法的参数选择方法,并着重研究降噪阶次和拉格朗日乘子对降噪效果的影响。齿轮故障仿真信号和齿轮早期裂纹故障振动信号的试验结果表明,μ-SVD降噪算法在降噪效果方面要优于传统的SVD降噪算法,可以在强背景噪声情况下更好地提取出齿轮的故障特征。%In order to extract machinery fault characteristics that are submerged in strong background noise, a general singular value decomposition (SVD) based subspace noise reduction algorithm is applied to signal processing, i.e.,μ-SVD based denoising method. It can be proved that the traditional SVD based denoising method is a special case of theμ-SVD based one whereμ=0.μ-SVD based denoising methodcontains a filter factor that plays a role in restraining information contributions of the noise-domain singular values to the denoised signal.μ-SVD based denoising method involves five parameters, including delay time, embedding dimension, noise reduction order, noise power and Lagrange multiplier. The selection methods for these parameters are discussed. In particular, the effects of noise reduction order and Lagrange multiplier on denoising performance are also studied. The experimental results of simulation signal with local fault and vibration signal with early crack fault in gear demonstrate that theμ-SVD based denoising method is superior to the

  17. Chambolle's Projection Algorithm for Total Variation Denoising

    Directory of Open Access Journals (Sweden)

    Joan Duran

    2013-12-01

    Full Text Available Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f=u+n, and n is supposed to be at each pixel independently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. This paper focuses on the simple description of the theory and on the implementation of Chambolle's projection algorithm for minimizing the total variation of a grayscale image. Furthermore, we adapt the algorithm to the vectorial total variation for color images. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.

  18. About Classification Methods Based on Tensor Modelling for Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Salah Bourennane

    2010-03-01

    Full Text Available Denoising and Dimensionality Reduction (DR are key issue to improve the classifiers efficiency for Hyper spectral images (HSI. The multi-way Wiener filtering recently developed is used, Principal and independent component analysis (PCA; ICA and projection pursuit(PP approaches to DR have been investigated. These matrix algebra methods are applied on vectorized images. Thereof, the spatial rearrangement is lost. To jointly take advantage of the spatial and spectral information, HSI has been recently represented as tensor. Offering multiple ways to decompose data orthogonally, we introduced filtering and DR methods based on multilinear algebra tools. The DR is performed on spectral way using PCA, or PP joint to an orthogonal projection onto a lower subspace dimension of the spatial ways. Weshow the classification improvement using the introduced methods in function to existing methods. This experiment is exemplified using real-world HYDICE data. Multi-way filtering, Dimensionality reduction, matrix and multilinear algebra tools, tensor processing.

  19. 基于改进双树复小波的光谱去噪算法研究%Research on spectrum denoising based on improved dual-tree complex wavelet transform

    Institute of Scientific and Technical Information of China (English)

    张立国; 胡永涛; 张淑清; 李军锋; 吴迪; 姜万录

    2016-01-01

    In order to remove the noise in the visible and near infrared spectra and to improve the accuracy of information extraction with the spectrum,an improved dual-tree complex wavelet transform(DTCWT)denoising method based on maximum a posteriori (MAP) estimation and generalized morphological filter(GMF)is presented.Firstly,the noisy signal is decomposed into high frequency and low frequency parts with the DTCWT.Then,MAP estimation and GMF are adopted for high frequency and low frequency denoising respectively.Finally,the denoised spectrum is obtained by reconstituting denoised high frequency and low frequency parts.Vegetables and almandineis spectra from the USGS spectral library are used in experiments,and the results show that the proposed method is ideal for denoising,which is easier to implement.A good denoising method is provided for visible and near infrared spectra.%为了消除可见光近红外光谱噪声,提高利用光谱曲线进行信息提取的精度,提出一种改进双树复小波变换(DTCWT)的后验估计及广义形态滤波的光谱去噪方法。首先对带噪信号进行双树复小波分解,将信号的高频部分和低频部分进行分离。然后分别采用最大后验(MAP)估计算法和广义形态学滤波(GMF)对高频系数和低频系数进行去噪处理。最后对去噪后的高频系数和低频系数进行 DTCWT 反变换,得到去噪光谱。对 USGS 光谱库中的植被光谱以及铁铝榴石光谱进行实验,结果表明该方法易于实现,去噪效果理想,是一种很好的可见光近红外光谱去噪方法。

  20. A Semi-implicit Image Denoising Algorithm in Matrix Form%一种基于矩阵格式的半隐式图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    石玉英; 刘晶晶

    2012-01-01

    图像去噪是进一步处理图像的必要步骤和关键环节之一.首先针对Rudin等在1992年提出的ROF模型,利用Crank—Nicolson半隐式差分格式进行离散,克服了显式离散格式的不稳定性和迭代次数多的缺点;其次在求解过程中提出了一种基于矩阵格式的半隐式新算法,并将新算法应用于三种边界条件——零边界条件、周期边界条件和Neumann边界条件进行数值试验;数值试验结果表明采用Crank—Nicolson半隐武离散格式去噪的效果优于显式离散格式;同时,Neumann边界条件能很好的保持图像边界的连续性.%The image denoising is one of the essential steps and key links in the process of image pro-cessing. Firstly, the Crank-Nicolson semi-implicit difference scheme is applied to discrete the famous Ru- din-Osher-Fatemi model which was proposed by Rudin et al. in 1992, overcoming the shortcomings of in- stability and many iterative numbers that the explicit discrete scheme has. Secondly, a semi-implicit image denoising algorithm in the matrix form is proposed. In the numerical experiment, we adopt Dirichlet boundary conditions, Periodic boundary conditions and Neumann boundary conditions. The experimental results show that the Crank-Nicolson semi-implicit scheme in matrix form is efficient and the Neumann boundary conditions keep the continuity of boundary.

  1. Variational denoising method for electronic speckle pattern interferometry

    Institute of Scientific and Technical Information of China (English)

    Fang Zhang; Wenyao Liu; Chen Tang; Jinjiang Wang; Li Ren

    2008-01-01

    Traditional speckle fringe patterns by electronic speckle pattern interferometry (ESPI) are inherently noisy and of limited visibility, so denoising is the key problem in ESPI. We present the variational denoising method for ESPI. This method transforms the image denosing to minimizing an appropriate penalized energy function and solving a partial differential equation. We test the proposed method on computer-simulated and experimental speckle correlation fringes, respectively. The results show that this technique is capable of significantly improving the quality of fringe patterns. It works well as a pre-processing for the fringe patterns by ESPI.

  2. Image restoration with surface-based fourth-order partial differential equation

    Science.gov (United States)

    Lu, Bibo; Liu, Qiang

    2010-07-01

    This paper presents an edge-preserving fourth order partial differential equation (PDE) for image restoration derived from a new surface-based energy functional. The corresponding fourth order PDE can preserve edges and avoid the staircase effect. The proposed model contains a function of gradient norm as an edge detector, which controls the diffusion speed according to the local structure of the image and preserves more details. Denoising results are given and we have also compared our method with some related PDE models.

  3. Low SNR image denoising via sparse and redundant representations over K-SVD algorithm and residual ratio iteration termination%基于K-SVD和残差比的低信噪比图像稀疏表示去噪算法

    Institute of Scientific and Technical Information of China (English)

    张晓阳; 柴毅; 李华锋

    2012-01-01

    针对低信噪比图像去噪问题,提出了一种基于K-SVD(Singular Value Decomposition)和残差比(Residual Ratio Iteration Termination)的正交匹配追踪(Orthogonal Matching Pursuit,OMP)图像稀疏分解去噪算法.该算法利用K-SVD算法将离散余弦变换(Discrete cosine transform,DCT)框架产生的冗余字典训练成能够有效反映图像结构特征的超完备字典,以实现图像的有效表示.然后以残差比作为OMP算法迭代的终止条件来实现图像的去噪.实验表明,该算法相对于传统基于Symlets小波图像去噪、基于Contourlet变换的图像去噪,以及基于DCT冗余字典的稀疏表示图像去噪,能够更加有效地滤除低信噪比图像中的高斯白噪声,保留原图像的有用信息.%For the low SNR( Signal to Noise Ratio) images denoising, a new algorithm is proposed based on K-SVD and residual ratio iteration termination. Firstly, an initial redundant dictionary is produced under the DCT framework and the dictionary is trained by K-SVD algorithm through the noisy image. A new dictionary that reflects the structure of the image effectively is produced. Then, the residual ratio is used as the iteration termination of OMP algorithm to remove the zero-mean white and homogeneous Gaussian additive noise from a given image. Different kinds of images with different noise levels are used to test the algorithm. The results show that the algorithm has strong robustness and performs better than the image denoising algorithm using Symlets wavelet, Contourlet and sparse representation based on DCT redundant dictionary.

  4. Research on speech denoising algorithm based on LMS adaptive noise cancellation and wavelet threshold%基于LMS自适应噪声抵消和小波阈值的语音降噪算法研究

    Institute of Scientific and Technical Information of China (English)

    柯水霞; 李迟生

    2016-01-01

    For the speech denoising disturbed with the broadband noise,a speech denoising algorithm based on LMS adap⁃tive noise cancellation and wavelet threshold is presented. The part noise is cancelled by LMS adaptive noise canceller used in the algorithm to obtain the speech signal with higher signal to noise ratio(SNR),and then the wavelet analysis for the signal is con⁃ducted. A new threshold function is used to denoise the single,and the denoised single is obtained by reconstruction. The experi⁃ment results of Matlab simulation show that the algorithm is better than single algorithm,and can avoid the“musical noise”caused by the traditional spectral subtraction. The visual effect,output SNR and root mean square error(RMSE)were improved greatly.%针对宽带噪声干扰的语音降噪问题,提出一种基于LMS自适应噪声抵消和小波阈值的语音降噪算法。该算法首先采用LMS自适应噪声抵消器对消部分噪声,得到较高信噪比的语音信号后,再对其进行小波分析,用一种新的阈值函数对信号进行降噪,再重构得到降噪后的信号。Matlab仿真实验证明,该算法的效果优于单一算法,且避免了传统谱减法带来的“音乐噪声”,视觉效果、输出信噪比和均方根误差也有很大改善。

  5. 基于小波块阈值降噪的OFDM系统信道估计算法%Channel Estimation Based on Block-thresholding Wavelet Denoising for OFDM System

    Institute of Scientific and Technical Information of China (English)

    杨永立; 刘建; 朱光喜

    2011-01-01

    A block-thresholding wavelet denoising based channel estimation algorithm was proposed.The algorithm improved the channel estimation performance by denoising the channel frequency function estimated by Least Square Estimator using block thresholding wavelet denoising.Compared with the conventional algorithms, block thresholding wavelet denoising algorithm ultilized the local correlation of the wavelet coefficients, so using the proposed method the mean square error and symbol error rate can be reduced greatly,and the computation complexity is only linearly proportional to the number effective subcarriers.The proposed method also poses good robustness when the CP length of the system is shorter than the delay spread of the channel.Numeric simulations show the performance improvement of the proposed algorithm to the conventional ones.%提出了基于小波块阈值降噪的OFDM信道估计算法.该算法通过对最小二乘信道估计算法的结果进行小波块阚值降噪来提高信道估计性能.与传统信道估计算法相比,块阈值降噪算法由于利用了信道频率响应小波系数的局部相关性,在仅增加少量运算量的前提下,大大降低了信道估计的均方误差、系统的误符号率和计算复杂度,运算量仅正比于有效子载波数,且在系统CP长度小于信道多径时延扩展时算法仍然可以保持很好的性能.数值仿真结果证明了上述结论的正确性.

  6. Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition

    Directory of Open Access Journals (Sweden)

    Rongbing Huang

    2016-01-01

    Full Text Available Based on a special type of denoising autoencoder (DAE and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR. Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures. Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images. The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors. Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively. Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion.

  7. 含噪信号的特征基表示与信号去噪重构%Characteristic Base Representation of Noisy Signal and Reconstruction of Denoised Signal

    Institute of Scientific and Technical Information of China (English)

    孙亮; 陈梅莲

    2000-01-01

    A denoising reconstruction algorithm using the signal magnitude spectrum is proposed and the simulation studies have been performed. The magnitude-frequency characteristics of the noisy signal is utilized in the algorithm and the denoising is realized by the use of characteristic bases. The simulation results have shown that the wide-band noise can be largely cancelled using the algorithm proposed to perform the noisy signal reconstruction. The algorithm can be used for the off-line denoising of the strong noise corrupted signals.%提出了一种利用信号幅度谱的去噪重构算法,并作了仿真实验研究. 该算法利用含噪信号在幅频特性上的特征,通过特征基来进行信号的去噪处理. 仿真研究结果表明,采用该种算法作含噪信号的重构,可以明显地去除信号的宽带噪声. 该算法可以应用于噪声严重污染信号的离线去噪处理.

  8. 融合MAR的偏微分方程声纳图像滤波去噪方法%Sonar image denoising method in combination with partial differential equation and MAR

    Institute of Scientific and Technical Information of China (English)

    李轲; 黄亮; 李翀伦

    2013-01-01

    To solve the problems of the excessive diffusion and the equation aberrance caused by inac-curate parameters which often appear in the use of the traditional PM (Perona-Malik)filtering model to deal with sonar images ,this paper proposes an image denoising method called MAR-PM in combi-nation with partial differential equation and MAR (Multi-scale AutoRegressive) .First ,MAR is used to obtain a series of multi-scale images .Then ,the PM method is adopted to filter the images on every scale so as to predict the finest-scale image .Using the predicted image as priori information ,it is pos-sible to denoise the original image by PM filtering .Finally ,the PSNR value is used to evaluate the effect of the method .The experiment result indicates that the method can eliminate the sonar image noises better in a shorter time than any other traditional methods ,so that it will have a good prospect of engineering application .%针对传统PM (Perona-Malik)滤波去噪模型在处理声纳图像时存在过度扩散和因参数设置不当导致方程畸变等问题,提出了一种融合多尺度自回归MAR(multi-scale autoregressive)模型和各向异性扩散偏微分方程PM模型的声纳图像滤波去噪方法MAR-PM。该方法首先利用 MAR得到多尺度图像序列,然后对每一级图像进行PM 滤波,并预测出最细尺度图像的滤波效果,再以此为先验信息指导对原始噪声图像的PM 滤波去噪,最后用 PS N R值对处理效果进行评价。试验结果表明:该方法不仅有效提高了声纳图像的滤波效果,而且缩短了处理时间。

  9. Mammogram contrast enhancement in wavelet domain using fuzzy denoising

    Directory of Open Access Journals (Sweden)

    Hadi Amirpour

    2016-06-01

    Full Text Available Breast cancer is one of the leading death causes among many women around the world. Diagnosing breast cancer in early stage before it gets the chance to spread helps patients treatment.Mammography is an effective screening test that helps to early detection and diagnosis of breast cancer in women. In many cases, due to the low-energy X-ray beams usedin mammography and microcalcifications position, mammograms are low contrast and noisy images and it is difficult for radiologiststo distinguish between normal and neoplastic breast tissues.Therefore, image enhancement algorithms have been proposed to improve mammograms quality for better detection. The main focus of this paper is to propose a procedure for enhancingthe contrast of mammogram images by using a fuzzy based wavelet transform. At the first step of the procedure, a multiscale wavelet transform is obtained in four levels. In the second step,the fuzzy logic approach is applied to the fourth level for denoising and improving image quality. In the final step, wavelet coefficients in all scales are manipulated by an enhancing factor, to improve the image contrast. The performance of the proposed procedure was investigated according to various factors by simulation data.

  10. The Application of Compressive Sensing on Spectra De-noising

    Directory of Open Access Journals (Sweden)

    Mingxia Xiao

    2013-10-01

    Full Text Available Through the analyzing of limitations on wavelet threshold filter de-noising, this paper applies wavelet filter based on compressed sensing to reduce the signal noise of spectral signals, and compares the two methods through experiments. The results of experiments shown that the wavelet filter based on compressed sensing can effectively reduce the signal noise of spectral signal. The de-noising effect of the method is better than that of wavelet filter. The method provides a new approach for reducing the signal noise of spectral signals.

  11. CUDA-based acceleration of collateral filtering in brain MR images

    Science.gov (United States)

    Li, Cheng-Yuan; Chang, Herng-Hua

    2017-02-01

    Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.

  12. 基于优化SVD和平稳小波的复合降噪方法%Compound De-noising Method Based on Discrete Stationary Wavelet Transform and Optimized SVD

    Institute of Scientific and Technical Information of China (English)

    黄建招; 谢建; 李锋; 李良

    2013-01-01

    通过分析奇异值分解(SVD)和小波阈值两种降噪方法的特点,提出了基于优化奇异值分解和平稳小波的复合降噪方法.方法采用统计学习理论的结构风险最小化原则优化确定矩阵有效秩,解决奇异值分解降噪特征值选取困难的问题.针对传统二进离散小波忽略尺度噪声且在奇异点存在振荡效应的不足,运用改进的平稳小波降噪方法对奇异值分解降噪后的信号进行精细处理.通过在不同信噪比条件下与传统离散二进小波进行降噪对比试验,证明了方法的有效性和优越性.%According to the analysis of characteristics of the SVD and wavelet threshold de-noising methods,the compound de-noising method based on discrete stationary wavelet transform and optimized SVD was put forward.The effective rank of the matrix is optimized by the structural risk minimization principal of the statistical learning theory,solving the problem of choosing SVD de-noising eigenvalue.Aiming to the shortage of the oscillation effects of traditional discrete binary wavelet transform in singularity and ignoring the noise influence of the approximation coefficients,the improved discrete stationary wavelet transform method was used to do threshold de-noising after the SVD de-noising.At last,the de-noising experiments are carried out compared with the traditional discrete binary wavelet transform under different conditions.The validity and superiority of the method are proved by the experiment.

  13. 基于经验模式分解法的光学相干层析成像去噪研究%Study of de-noising based on empirical mode decomposition method in optical coherence tomography

    Institute of Scientific and Technical Information of China (English)

    张仙玲; 高万荣; 卞海溢; 卢邱生

    2012-01-01

    针对光学相干层析(OCT,opticalcoherencetomography)成像中存在的散斑噪声和扫描噪声,提出了采用经验模式分解(EMD,empiricalmodedecomposition)算法同时减小这两种噪声的思想。EMD是一种时频分析法,较傅立叶谱法能准确地确定时变非平稳的这两种噪声随时间变化的频率特性,从而获得更好的滤波效果。结果表明,通过合理设计EMD滤波参数,即可有效地同时减小散斑噪声和扫描噪声,信号的信噪比(SNR)提高(不考虑扫描噪声时,SNR达7dB左右,考虑到扫描噪声时,SNR提高达3dB左右),扫描噪声的条纹对比度降低60%以上,改善了成像质量,同时图像细节得到保留。与小波去噪法相比,本文方法具有滤波器设计简单、去噪效果明显及能同时有效地去除两种噪声的优点。%Optical coherence tomography (OCT) has been shown to have advantages for obtaining images of biological tissues noninvasively and has great potential for applications in medical diagnosis. In this work,a method based on the empirical mode decomposition (EMD) method is adapted to improve OCT image quality by reducing the effects of noises. EMD is a time-frequency analysis, So it can more accurately analyze the time-varying non stationary noise signal compared with the domain transform method. The experimental results show that EMD method can effectively suppress speckle noise,which improves signal-to-noise (SNR) to 7 dB and 3 dB for absence and presence of the scanner noise. It can also effectively remove the stripe noise due to instability of the scanning reference mirror in OCT as long as the EMD filter parameter is set properly. The fringe contrast can be reduced by more than 60 %. The quality of OCT images is improved. Compared with the wavelet de-noising method,EMD has the advantages of simple operation steps, obvious de-noising effect, and the ability of effectively removing two

  14. 基于偏微分方程的低剂量CT投影降噪算法%DENOISING ALGORITHM FOR LOW-DOSE CT PROJECTION BASED ON PARTIAL DIFFERENTIAL EQUATION

    Institute of Scientific and Technical Information of China (English)

    唐瑜; 崔学英; 张权; 刘祎; 桂志国

    2014-01-01

    Because of using lower X-ray dose,the harm of radiation in low-dose CT image on the human body is greatly reduced.But the problem it brings out is that the projection data are polluted seriously by the noise,thus leads to the reductions in quality of the reconstructed image.In view of the above problems,based on partial differential equations we present an improved projectiond domain denoising algorithm. On the basis of the anisotropic diffusion equation,the algorithm can effectively reflect the feature of local characteristics of the image by using the local entropy to control the degree of diffusion.Experimental results show that the new algorithm better preserves the detail and edge information of the reconstructed image while increasing the SNR of reconstructed image.%低剂量 CT 图像由于采用了较低的 X 射线放射剂量,大大降低了辐射对人体的危害,但由此带来的问题是投影数据受噪声污染严重,从而导致了重建图像质量的降低。针对上述问题,在基于偏微分方程的基础上,提出一种改进的投影域降噪算法。该算法在各向异性扩散方程的基础上,利用局部熵可以有效地反映图像局部特征的特点,来控制扩散的程度。实验结果表明,新的算法在提高重建图像信噪比的同时更好地保持了图像的细节和边缘信息。

  15. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  16. Wavelet-based method for image filtering using scale-space continuity

    Science.gov (United States)

    Jung, Claudio R.; Scharcanski, Jacob

    2001-04-01

    This paper proposes a novel technique to reduce noise while preserving edge sharpness during image filtering. This method is based on the image multiresolution decomposition by a discrete wavelet transform, given a proper wavelet basis. In the transform spaces, edges are implicitly located and preserved, at the same time that image noise is filtered out. At each resolution level, geometric continuity is used to preserve edges that are not isolated. Finally, we compare consecutive levels to preserve edges having continuity along scales. As a result, the proposed technique produces a filtered version of the original image, where homogeneous regions appear segmented by well-defined edges. Possible applications include image presegmentation and image denoising.

  17. 散乱点云噪声分析与降噪方法研究%Denoising Methods Research on Scattered Point Cloud Based on Noise Analysis

    Institute of Scientific and Technical Information of China (English)

    王振; 孙志刚

    2015-01-01

    As main part of the preprocessing of scattered point cloud ,outlier identification and surface smoothing are important premise of 3D modeling and visualization .In order to solve this problem ,some certain effective denoising methods are put forward based on the characteristics of random measurement error and noise distribution of point cloud :identifying outliers by mapping the point cloud into feature space through feature extraction ;surface smoothing estimates the true loca‐tions of sampling points based on the noise distribution .The experimental results indicate that the proposed methods can i‐dentify outliers accurately and smooth model surface effectively .%散乱点云离群点识别和表面平滑作为点云预处理的主要组成部分,是三维场景建模和可视化的重要前提。针对这一问题,论文提出了基于随机测量误差特点和噪声点分布特性的点云降噪方法:离群点识别采用统计分类的思想,通过特征提取将点云映射到特征空间后加以区分;表面平滑利用噪声点分布特性,对采样点的真实位置进行估计。实验结果表明,采用文中的方法能够准确有效地识别离群点和平滑模型表面。

  18. [A fractal denoising method for astronomical spectral signal].

    Science.gov (United States)

    Han, Jin-shu; Luo, A-li; Zhao, Yong-heng

    2011-12-01

    To restore the continuum and the spectral lines from a noisy astronomical spectrum, then to measure the equivalent widths of the spectral lines, the fractal denoising method was firstly used in astronomical spectra in the present paper. The method is based on the distinguishing features, that is the local self-similarities exist in an astronomical spectrum, while not in a random white noise signal. The experimental results show that the fractal denoising method is efficient in parameter measurements, such as equivalent widths for spectral lines, redshift of galaxies, and so on. In addition, the method can achieve data compression. The fractal method can be used in the mass spectra of LAMOST.

  19. Discrete shearlet transform on GPU with applications in anomaly detection and denoising

    Science.gov (United States)

    Gibert, Xavier; Patel, Vishal M.; Labate, Demetrio; Chellappa, Rama

    2014-12-01

    Shearlets have emerged in recent years as one of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localized functions defined not only over a range of scales and locations, but also over a range of orientations and with highly anisotropic supports. As a result, shearlets are much more effective than traditional wavelets in handling the geometry of multidimensional data, and this was exploited in a wide range of applications from image and signal processing. However, despite their desirable properties, the wider applicability of shearlets is limited by the computational complexity of current software implementations. For example, denoising a single 512 × 512 image using a current implementation of the shearlet-based shrinkage algorithm can take between 10 s and 2 min, depending on the number of CPU cores, and much longer processing times are required for video denoising. On the other hand, due to the parallel nature of the shearlet transform, it is possible to use graphics processing units (GPU) to accelerate its implementation. In this paper, we present an open source stand-alone implementation of the 2D discrete shearlet transform using CUDA C++ as well as GPU-accelerated MATLAB implementations of the 2D and 3D shearlet transforms. We have instrumented the code so that we can analyze the running time of each kernel under different GPU hardware. In addition to denoising, we describe a novel application of shearlets for detecting anomalies in textured images. In this application, computation times can be reduced by a factor of 50 or more, compared to multicore CPU implementations.

  20. Semantic-based high resolution remote sensing image retrieval

    Science.gov (United States)

    Guo, Dihua

    High Resolution Remote Sensing (HRRS) imagery has been experiencing extraordinary development in the past decade. Technology development means increased resolution imagery is available at lower cost, making it a precious resource for planners, environmental scientists, as well as others who can learn from the ground truth. Image retrieval plays an important role in managing and accessing huge image dat