A simple denoising algorithm using wavelet transform
Roy, M F; Kulkarni, B D; Sanderson, J; Rhodes, M; Stappen, M; Roy, Manojit; Sanderson, John; Rhodes, Martin; Stappen, Michel vander
1999-01-01
We have presented a new and alternative algorithm for noise reduction using the methods of discrete wavelet transform and numerical differentiation of the data. In our method the threshold for reducing noise comes out automatically. The algorithm has been applied to three model flow systems - Lorenz, Autocatalator, and Rossler systems - all evolving chaotically. The method is seen to work quite well for a wide range of noise strengths, even as large as 10% of the signal level. We have also applied the method successfully to noisy time series data obtained from the measurement of pressure fluctuations in a fluidized bed, and also to that obtained by conductivity measurement in a liquid surfactant experiment. In all the illustrations we have been able to observe that there is a clean separation in the frequencies covered by the differentiated signal and white noise.
A Distortion Input Parameter in Image Denoising Algorithms with Wavelets
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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.
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.
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...
Wavelet Based Image Denoising Technique
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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, Poissonâ€™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.
Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV
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.
HYDRAULIC PRESSURE SIGNAL DENOISING USING THRESHOLD SELF-LEARNING WAVELET ALGORITHM
Institute of Scientific and Technical Information of China (English)
GUO Xin-lei; YANG Kai-lin; GUO Yong-xin
2008-01-01
A pre-filter combined with threshold self-learning wavelet algorithm is proposed for hydraulic pressure signals denoising. The denoising threshold is self-learnt in the steady flow state, and then modified under a given limit to make the mean square errors between reconstruction signals and desirable outputs minimum, so the corresponding optimal denoising threshold in a single operating case can be obtained. These optimal thresholds are used for the whole signal denoising and are different in various cases. Simulation results and comparative studies show that the present approach has an obvious effect of noise suppression and is superior to those of traditional wavelet algorithms and back-propagation neural networks. It also provides the precise data for the next step of pipeline leak detection using transient technique.
Implemented Wavelet Packet Tree based Denoising Algorithm in Bus Signals of a Wearable Sensorarray
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.
A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
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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.
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.
Method and application of wavelet shrinkage denoising based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
无
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.
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.
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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.
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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.
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.
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.
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.
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.
Image denoising based on wavelet cone of influence analysis
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.
Terahertz digital holography image denoising using stationary wavelet transform
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.
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.
A Fast Wavelet Multilevel Approach to Total Variation Image Denoising
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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.
A Comparative Study of Wavelet Thresholding for Image Denoising
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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.
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.
Shukla, K K
2013-01-01
Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated
Region-based image denoising through wavelet and fast discrete curvelet transform
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.
Denoising CT Images using wavelet transform
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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.
Single Channel Speech Enhancement by De-noising Using Stationary Wavelet Transform
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. This de-noising algorithm offers a superior performance to speech signal noise suppress.
Boosting of Image Denoising Algorithms
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 ...
Improved deadzone modeling for bivariate wavelet shrinkage-based image denoising
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.
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.
Dual tree complex wavelet transform based denoising of optical microscopy images.
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.
Optimal wavelet denoising for smart biomonitor systems
Messer, Sheila R.; Agzarian, John; Abbott, Derek
2001-03-01
Future smart-systems promise many benefits for biomedical diagnostics. The ideal is for simple portable systems that display and interpret information from smart integrated probes or MEMS-based devices. In this paper, we will discuss a step towards this vision with a heart bio-monitor case study. An electronic stethoscope is used to record heart sounds and the problem of extracting noise from the signal is addressed via the use of wavelets and averaging. In our example of heartbeat analysis, phonocardiograms (PCGs) have many advantages in that they may be replayed and analysed for spectral and frequency information. Many sources of noise may pollute a PCG including foetal breath sounds if the subject is pregnant, lung and breath sounds, environmental noise and noise from contact between the recording device and the skin. Wavelets can be employed to denoise the PCG. The signal is decomposed by a discrete wavelet transform. Due to the efficient decomposition of heart signals, their wavelet coefficients tend to be much larger than those due to noise. Thus, coefficients below a certain level are regarded as noise and are thresholded out. The signal can then be reconstructed without significant loss of information in the signal. The questions that this study attempts to answer are which wavelet families, levels of decomposition, and thresholding techniques best remove the noise in a PCG. The use of averaging in combination with wavelet denoising is also addressed. Possible applications of the Hilbert Transform to heart sound analysis are discussed.
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.
Directory of Open Access Journals (Sweden)
Ali Shaban
2017-07-01
Full Text Available Speech signals play a significant role in the area of digital signal processing. When these signals pass through air as a channel of propagation, it interacts with noise. Therefore, it needs removing noise from corrupted signal without altering it. De-noising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To improve the performance of the speech which displays high power fluctuations, a new speech de-noising method based on Invasive Weed Optimization (IWO is proposed. In addition, a theoretical model is modified to estimate the value of threshold without any priority of knowledge. This is done by implementing the IWO algorithm for kurtosis measuring of the residual noise signal to find an optimum threshold value at which the kurtosis function is maximum. It has been observed that the proposed method appeared better performance than other methods at the same condition. Moreover, the results show that the proposed IWO algorithm offered a better mean square error(MSE than Particle Swarm Optimization Algorithm (PSO for both one and multilevel decomposition. For instance, IWO brought an improvement in MSE in the range of 0.01 compared with PSO for multilevel decomposition.
Electrocardiogram signal denoising based on a new improved wavelet thresholding
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.
Denoising and robust nonlinear wavelet analysis
Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.
1994-03-01
In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transform, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the 'S+WAVELETS' object-oriented toolkit for wavelet analysis.
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.
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。
De-noising of digital image correlation based on stationary wavelet transform
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.
Denoising solar radiation data using coiflet wavelets
Energy Technology Data Exchange (ETDEWEB)
Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my; Janier, Josefina B., E-mail: josefinajanier@petronas.com.my; Muthuvalu, Mohana Sundaram, E-mail: mohana.muthuvalu@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia); Hasan, Mohammad Khatim, E-mail: khatim@ftsm.ukm.my [Jabatan Komputeran Industri, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor (Malaysia); Sulaiman, Jumat, E-mail: jumat@ums.edu.my [Program Matematik dengan Ekonomi, Universiti Malaysia Sabah, Beg Berkunci 2073, 88999 Kota Kinabalu, Sabah (Malaysia); Ismail, Mohd Tahir [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Minden, Penang (Malaysia)
2014-10-24
Signal denoising and smoothing plays an important role in processing the given signal either from experiment or data collection through observations. Data collection usually was mixed between true data and some error or noise. This noise might be coming from the apparatus to measure or collect the data or human error in handling the data. Normally before the data is use for further processing purposes, the unwanted noise need to be filtered out. One of the efficient methods that can be used to filter the data is wavelet transform. Due to the fact that the received solar radiation data fluctuates according to time, there exist few unwanted oscillation namely noise and it must be filtered out before the data is used for developing mathematical model. In order to apply denoising using wavelet transform (WT), the thresholding values need to be calculated. In this paper the new thresholding approach is proposed. The coiflet2 wavelet with variation diminishing 4 is utilized for our purpose. From numerical results it can be seen clearly that, the new thresholding approach give better results as compare with existing approach namely global thresholding value.
Application of the dual-tree complex wavelet transform in biomedical signal denoising.
Wang, Fang; Ji, Zhong
2014-01-01
In biomedical signal processing, Gibbs oscillation and severe frequency aliasing may occur when using the traditional discrete wavelet transform (DWT). Herein, a new denoising algorithm based on the dual-tree complex wavelet transform (DTCWT) is presented. Electrocardiogram (ECG) signals and heart sound signals are denoised based on the DTCWT. The results prove that the DTCWT is efficient. The signal-to-noise ratio (SNR) and the mean square error (MSE) are used to compare the denoising effect. Results of the paired samples t-test show that the new method can remove noise more thoroughly and better retain the boundary and texture of the signal.
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.
Denoising in Wavelet Packet Domain via Approximation Coefficients
Directory of Open Access Journals (Sweden)
Zahra Vahabi
2012-01-01
Full Text Available In this paper we propose a new approach in the wavelet domain for image denoising. In recent researches wavelet transform has introduced a time-Frequency transform for computing wavelet coefficient and eliminating noise. Some coefficients have effected smaller than the other's from noise, so they can be use reconstruct images with other subbands. We have developed Approximation image to estimate better denoised image. Naturally noiseless subimage introduced image with lower noise. Beside denoising we obtain a bigger compression rate. Increasing image contrast is another advantage of this method. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.100 images of LIVE Dataset were tested, comparing signal to noise ratios (SNR,soft thresholding was %1.12 better than hard thresholding, POAC was %1.94 better than soft thresholding and POAC with wavelet packet was %1.48 better than POAC.
Wavelets Applied to CMB Maps a Multiresolution Analysis for Denoising
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 $S/N \\ge 1$.
Denoising time-domain induced polarisation data using wavelet techniques
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.
Image denoising with the dual-tree complex wavelet transform
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.
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.
Denoising functional MR images : A comparison of wavelet denoising and Gaussian smoothing
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
Study of Denoising in TEOAE Signals Using an Appropriate Mother Wavelet Function
Directory of Open Access Journals (Sweden)
Habib Alizadeh Dizaji
2007-06-01
Full Text Available Background and Aim: Matching a mother wavelet to class of signals can be of interest in signal analysis and denoising based on wavelet multiresolution analysis and decomposition. As transient evoked otoacoustic emissions (TEOAES are contaminated with noise, the aim of this work was to provide a quantitative approach to the problem of matching a mother wavelet to TEOAE signals by using tuning curves and to use it for analysis and denoising TEOAE signals. Approximated mother wavelet for TEOAE signals was calculated using an algorithm for designing wavelet to match a specified signal.Materials and Methods: In this paper a tuning curve has used as a template for designing a mother wavelet that has maximum matching to the tuning curve. The mother wavelet matching was performed on tuning curves spectrum magnitude and phase independent of one another. The scaling function was calculated from the matched mother wavelet and by using these functions, lowpass and highpass filters were designed for a filter bank and otoacoustic emissions signal analysis and synthesis. After signal analyzing, denoising was performed by time windowing the signal time-frequency component.Results: Aanalysis indicated more signal reconstruction improvement in comparison with coiflets mother wavelet and by using the purposed denoising algorithm it is possible to enhance signal to noise ratio up to dB.Conclusion: The wavelet generated from this algorithm was remarkably similar to the biorthogonal wavelets. Therefore, by matching a biorthogonal wavelet to the tuning curve and using wavelet packet analysis, a high resolution time-frequency analysis for the otoacoustic emission signals is possible.
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。
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.
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.
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。
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.
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.
Deasy, Joseph O; Wickerhauser, M Victor; Picard, Mathieu
2002-10-01
The Monte Carlo dose calculation method works by simulating individual energetic photons or electrons as they traverse a digital representation of the patient anatomy. However, Monte Carlo results fluctuate until a large number of particles are simulated. We propose wavelet threshold de-noising as a postprocessing step to accelerate convergence of Monte Carlo dose calculations. A sampled rough function (such as Monte Carlo noise) gives wavelet transform coefficients which are more nearly equal in amplitude than those of a sampled smooth function. Wavelet hard-threshold de-noising sets to zero those wavelet coefficients which fall below a threshold; the image is then reconstructed. We implemented the computationally efficient 9,7-biorthogonal filters in the C language. Transform results were averaged over transform origin selections to reduce artifacts. A method for selecting best threshold values is described. The algorithm requires about 336 floating point arithmetic operations per dose grid point. We applied wavelet threshold de-noising to two two-dimensional dose distributions: a dose distribution generated by 10 MeV electrons incident on a water phantom with a step-heterogeneity, and a slice from a lung heterogeneity phantom. Dose distributions were simulated using the Integrated Tiger Series Monte Carlo code. We studied threshold selection, resulting dose image smoothness, and resulting dose image accuracy as a function of the number of source particles. For both phantoms, with a suitable value of the threshold parameter, voxel-to-voxel noise was suppressed with little introduction of bias. The roughness of wavelet de-noised dose distributions (according to a Laplacian metric) was nearly independent of the number of source electrons, though the accuracy of the de-noised dose image improved with increasing numbers of source electrons. We conclude that wavelet shrinkage de-noising is a promising method for effectively accelerating Monte Carlo dose calculations
Low-dose computed tomography image denoising based on joint wavelet and sparse representation.
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.
Comparative study of wavelet denoising in myoelectric control applications.
Sharma, Tanu; Veer, Karan
2016-01-01
Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.
Institute of Scientific and Technical Information of China (English)
DENG Ke; ZHANG Lu; LUO Mao-Kang
2011-01-01
@@ Aiming at the shortage of conventional threshold function in wavelet noise reduction of chaotic signals, we propose a wavelet-packet noise reduction method of chaotic signals based on a new higher order threshold function.The method retains the useful high-frequency information, and the threshold function is continuous and derivable, therefore it is more consistent with the characteristics of the continuous signal.Contrast simulation experiment shows that the effect of noise reduction and the precision of noise reduction of chaotic signals both are improved.%Aiming at the shortage of conventional threshold function in wavelet noise reduction of chaotic signals, we propose a wavelet-packet noise reduction method of chaotic signals based on a new higher order threshold function. The method retains the useful high-frequency information, and the threshold function is continuous and derivable,therefore it is more consistent with the characteristics of the continuous signal. Contrast simulation experiment shows that the effect of noise reduction and the precision of noise reduction of chaotic signals both are improved.
Bitenc, M.; Kieffer, D. S.; Khoshelham, K.
2015-08-01
The precision of Terrestrial Laser Scanning (TLS) data depends mainly on the inherent random range error, which hinders extraction of small details from TLS measurements. New post processing algorithms have been developed that reduce or eliminate the noise and therefore enable modelling details at a smaller scale than one would traditionally expect. The aim of this research is to find the optimum denoising method such that the corrected TLS data provides a reliable estimation of small-scale rock joint roughness. Two wavelet-based denoising methods are considered, namely Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), in combination with different thresholding procedures. The question is, which technique provides a more accurate roughness estimates considering (i) wavelet transform (SWT or DWT), (ii) thresholding method (fixed-form or penalised low) and (iii) thresholding mode (soft or hard). The performance of denoising methods is tested by two analyses, namely method noise and method sensitivity to noise. The reference data are precise Advanced TOpometric Sensor (ATOS) measurements obtained on 20 × 30 cm rock joint sample, which are for the second analysis corrupted by different levels of noise. With such a controlled noise level experiments it is possible to evaluate the methods' performance for different amounts of noise, which might be present in TLS data. Qualitative visual checks of denoised surfaces and quantitative parameters such as grid height and roughness are considered in a comparative analysis of denoising methods. Results indicate that the preferred method for realistic roughness estimation is DWT with penalised low hard thresholding.
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.
Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model
Directory of Open Access Journals (Sweden)
Kaijian He
2014-01-01
Full Text Available Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH- based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models.
NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING
Institute of Scientific and Technical Information of China (English)
CHEN Zhixin; XU Jinwu; YANG Debin
2008-01-01
Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform (DT-CWT) is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio (SNR) than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals.
Wavelet-based denoising using local Laplace prior
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.
Research of Signal De-noising Technique Based on Wavelet
Shigang Hu; Yinglu Hu; Xiaofeng Wu; Jin Li; Zaifang Xi; Jin Zhao
2013-01-01
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...
Inertial Sensor Signals Denoising with Wavelet Transform
Directory of Open Access Journals (Sweden)
Ioana-Raluca EDU
2015-03-01
Full Text Available In the current paper we propose a new software procedure for processing data from an inertial navigation system boarded on a moving vehicle, in order to achieve accurate navigation information on the displacement of the vehicle in terms of position, speed, acceleration and direction. We divided our research in three phases. In the first phase of our research, we implemented a real-time evaluation criterion with the intention of achieving real-time data from an accelerometer. It is well-known that most errors in the detection of position, velocity and attitude in inertial navigation occur due to difficult numerical integration of noise. In the second phase, we were interested in achieving a better estimation and compensation of the gyro sensor angular speed measurements. The errors of these sensors occur because of their miniaturization, they cannot be eliminated but can be modelled by applying specific signal processing methods. The objective of both studies was to propose a signal processing algorithm, based on Wavelet filter, along with a criterion for evaluating and updating the optimal decomposition level of Wavelet transform for achieving accurate information from inertial sensors. In the third phase of our work we are suggesting the utility of a new complex algorithm for processing data from an inertial measurement unit, containing both miniaturized accelerometers and gyros, after undergoing a series of numerical simulations and after obtaining accurate information on vehicle displacement
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.
Oriented wavelet transform for image compression and denoising.
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.
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.
Impedance cardiography signal denoising using discrete wavelet transform.
Chabchoub, Souhir; Mansouri, Sofienne; Salah, Ridha Ben
2016-09-01
Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal. The Haar, Daubechies (db2, db4, db6, and db8), Symlet (sym2, sym4, sym6, sym8) and Coiflet (coif2, coif3, coif4, coif5) wavelet families are tested and evaluated in order to select the most suitable denoising method. The wavelet family with best performance is compared with two denoising methods: one based on Savitzky-Golay filtering and the other based on median filtering. Each method is evaluated by means of the signal to noise ratio (SNR), the root mean square error (RMSE) and the percent difference root mean square (PRD). The results show that the Daubechies wavelet family (db8) has superior performance on noise reduction in comparison to other methods.
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
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.
Application of Wavelet Denoising Algorithm in Noisy Blind Source Separation%小波去噪算法在含噪盲源分离中的应用
Institute of Scientific and Technical Information of China (English)
吴微; 彭华; 王彬
2015-01-01
Blind source separation (BSS) algorithms based on the noise‐free model are not applicable when the SNR is low .To deal with this issue ,one way is to denoise the mixtures corrupted by white Gaussian noise ,firstly ,and then utilize the BSS algorithms .Therefore ,a Waveshrink algorithm is proposed based on translation invariant to denoise mixtures with strong noise .The high‐frequency coefficients sliding window method is utilized to estimate the noise variance accurately ,and BayesShrink algorithm is utilized for a more reasonable threshold .Consequently ,the scope of the translation invariant is narrowed without degrading the performance of denoising ,thus reducing the computation amount .Simulation results indi‐cate that the proposed approach perform better in denoising compared with the traditional Waveshrink al‐gorithm ,and can remarkably enhance the separation performance of BSS algorithms ,especially in the case with low signal SNRs .%无噪模型下的盲源分离算法在信噪比较低的情况下并不适用。针对该情况一种解决方案就是先对含有高斯白噪声的混合信号进行去噪预处理，然后使用盲源分离算法进行分离。为此，本文提出了一种适用于信噪比较低条件下的基于平移不变量的小波去噪算法。该算法首先使用高频系数滑动窗口法准确估计含噪混合信号的噪声方差，然后使用Bayesshrink阈值估计算法得到更加合理的阈值，最后在不降低去噪效果的同时缩小了平移不变量的范围，减少了运算量。实验仿真表明，在信噪比较低的情况下，与传统小波去噪算法相比，该算法可以更加有效地去除噪声，在很大程度上提升盲源分离算法的性能。
Study on adaptive thresholding technique of image denoising based on wavelet transform
Zhu, Xi'an; Xie, Xiao
2011-05-01
It is studied in the paper that an adaptive soft and hard thresholding image denoising method, in which image pyramid decomposing is realized by wavelet transform, and the mean value, mid-value and root mean square value of different sub bands are calculated as thresholding. The image is added into different kinds and different intensities noise, and processed by different wavelet decomposing levels and thresholding selected algorithms, the total 27 kinds of thresholding combination schemes are completed in the research process. The SNR (signal noise ratio) and PSNR (peak signal noise ratio) of denoised image are compared and analyzed and benefited results are achieved. Furthermore, the algorithm in reference is realized by MATLAB program, the results of reference are compared with that of the paper to demonstrate the significance and correctness of the results in the paper.
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.)
Automated wavelet denoising of photoacoustic signals for burn-depth image reconstruction
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.
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.
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.
Morphology of the galaxy distribution from wavelet denoising
Martínez, V J; Saar, E; Donoho, D L; Reynolds, S; De la Cruz, P; Paredes, S
2005-01-01
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments and hotspots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provide us with a unique and more accurate morphological description.
Morphology of the Galaxy Distribution from Wavelet Denoising
Martínez, Vicent J.; Starck, Jean-Luc; Saar, Enn; Donoho, David L.; Reynolds, Simon C.; de la Cruz, Pablo; Paredes, Silvestre
2005-11-01
We have developed a method based on wavelets to obtain the true underlying smooth density from a point distribution. The goal has been to reconstruct the density field in an optimal way, ensuring that the morphology of the reconstructed field reflects the true underlying morphology of the point field, which, as the galaxy distribution, has a genuinely multiscale structure, with near-singular behavior on sheets, filaments, and hot spots. If the discrete distributions are smoothed using Gaussian filters, the morphological properties tend to be closer to those expected for a Gaussian field. The use of wavelet denoising provides us with a unique and more accurate morphological description.
Optimization of dynamic measurement of receptor kinetics by wavelet denoising.
Alpert, Nathaniel M; Reilhac, Anthonin; Chio, Tat C; Selesnick, Ivan
2006-04-01
The most important technical limitation affecting dynamic measurements with PET is low signal-to-noise ratio (SNR). Several reports have suggested that wavelet processing of receptor kinetic data in the human brain can improve the SNR of parametric images of binding potential (BP). However, it is difficult to fully assess these reports because objective standards have not been developed to measure the tradeoff between accuracy (e.g. degradation of resolution) and precision. This paper employs a realistic simulation method that includes all major elements affecting image formation. The simulation was used to derive an ensemble of dynamic PET ligand (11C-raclopride) experiments that was subjected to wavelet processing. A method for optimizing wavelet denoising is presented and used to analyze the simulated experiments. Using optimized wavelet denoising, SNR of the four-dimensional PET data increased by about a factor of two and SNR of three-dimensional BP maps increased by about a factor of 1.5. Analysis of the difference between the processed and unprocessed means for the 4D concentration data showed that more than 80% of voxels in the ensemble mean of the wavelet processed data deviated by less than 3%. These results show that a 1.5x increase in SNR can be achieved with little degradation of resolution. This corresponds to injecting about twice the radioactivity, a maneuver that is not possible in human studies without saturating the PET camera and/or exposing the subject to more than permitted radioactivity.
Denoising and robust non-linear wavelet analysis
Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.
1994-04-01
In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistance wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transforms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+Wavelets object-oriented toolkit for wavelet analysis.
Application of Wavelet Analysis in Signal De-noising of Blast Shock Wave Overpressure
Institute of Scientific and Technical Information of China (English)
Jian-wei JIANG; Yu-jun FANG; Li-zhen WAN; Jian-bing MEN
2010-01-01
It's a problem to be solved how to de-noise the signal of blast shock wave overpressure.In the conventional methods,the high frequency of the signal is cut directly by some mathematics algorithms,such as Fourier Transform,but some of the useful signal will be cut together.We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis.There are four steps in this method.Firstly,the original signal is de-cpmposed.Then the time-frequency features of the signal and noise are analyzed.Thirdly,the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible.Lastly,the useful signal with least loss of information is recovered by reconstruction process.To verify this method,a blast shock wave signal is de-noised with FFT to make a comparison.The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.
Energy-based wavelet de-noising of hydrologic time series.
Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu
2014-01-01
De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed.
Denoising portal images by means of wavelet techniques
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
ECG signal denoising via empirical wavelet transform.
Singh, Omkar; Sunkaria, Ramesh Kumar
2016-12-29
This paper presents new methods for baseline wander correction and powerline interference reduction in electrocardiogram (ECG) signals using empirical wavelet transform (EWT). During data acquisition of ECG signal, various noise sources such as powerline interference, baseline wander and muscle artifacts contaminate the information bearing ECG signal. For better analysis and interpretation, the ECG signal must be free of noise. In the present work, a new approach is used to filter baseline wander and power line interference from the ECG signal. The technique utilized is the empirical wavelet transform, which is a new method used to compute the building modes of a given signal. Its performance as a filter is compared to the standard linear filters and empirical mode decomposition.The results show that EWT delivers a better performance.
Wavelet Denoising and Surface Electromyography Analysis
Hussain, M.S.; Md. Mamun
2012-01-01
In this research, Surface Electromyography (SEMG) signal analysis from the right rectus femoris muscle is performed during walk. Wavelet Transform (WT) has been applied for removing noise from the surface SEMG. Gaussianity tests are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. Results show that the proposed method can effectively remove noise from the raw SEMG signals for further analysis.
Image denoising via fundamental anisotropic diffusion and wavelet shrinkage: a comparative study
Bayraktar, Bulent; Analoui, Mostafa
2004-05-01
Noise removal faces a challenge: Keeping the image details. Resolving the dilemma of two purposes (smoothing and keeping image features in tact) working inadvertently of each other was an almost impossible task until anisotropic dif-fusion (AD) was formally introduced by Perona and Malik (PM). AD favors intra-region smoothing over inter-region in piecewise smooth images. Many authors regularized the original PM algorithm to overcome its drawbacks. We compared the performance of denoising using such 'fundamental' AD algorithms and one of the most powerful multiresolution tools available today, namely, wavelet shrinkage. The AD algorithms here are called 'fundamental' in the sense that the regularized versions center around the original PM algorithm with minor changes to the logic. The algorithms are tested with different noise types and levels. On top of the visual inspection, two mathematical metrics are used for performance comparison: Signal-to-noise ratio (SNR) and universal image quality index (UIQI). We conclude that some of the regu-larized versions of PM algorithm (AD) perform comparably with wavelet shrinkage denoising. This saves a lot of compu-tational power. With this conclusion, we applied the better-performing fundamental AD algorithms to a new imaging modality: Optical Coherence Tomography (OCT).
Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage
Directory of Open Access Journals (Sweden)
Yan-long Chen
2014-01-01
Full Text Available The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.
Denoising method of heart sound signals based on self-construct heart sound wavelet
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.
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.
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.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.
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.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
Directory of Open Access Journals (Sweden)
Chaolong Zhang
2015-01-01
Full Text Available 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.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
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. PMID:26413090
Institute of Scientific and Technical Information of China (English)
Han Wenhua; Que Peiwen
2005-01-01
This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.
Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering
Institute of Scientific and Technical Information of China (English)
Shirong Yin; Weiran Wang
2006-01-01
Lidar is an effective tool for remotely monitoring target or object, but the lidar signal is often affected by various noises or interferences. Therefore, detecting the weak signals buried in noises is a fundamental and important problem in the lidar systems. In this paper, an effective noise reduction method combining wavelet improved threshold with wavelet domain spatial filtration is presented to denoise pulse lidar signal and is investigated by detecting the simulating pulse lidar signals in noise. The simulation results show that this method can effectively identify the edge of signal and detect the weak lidar signal buried in noises.
Channel Estimation Algorithm Based on Wavelet De-Noising and Improved DFT%基于小波去噪与改进的DFT信道估计算法
Institute of Scientific and Technical Information of China (English)
张开明
2016-01-01
In view of the problem that traditional channel estimation algorithm based on Discrete Fourier Transform (DFT) does not eliminate the noise in the cyclic prefix length, the paper puts forward a new method of Orthogonal Frequency Division Multiplexing (OFDM) system channel estimation based on wavelet de-noising and DFT interpolation. In simulation on Matlab 2012b platform, compared with the traditional channel estimation al-gorithm based on DFT, under the conditions of the same Bit-Error-Rate (BER) or the same Mean-Square-Error (MSE) the Signal-to-Noise Rate (SNR) performance of the proposed algorithm improves a lot. The simulation re-sults show that the proposed algorithm can reduce the influence of the noise, improve the accuracy of channel esti-mation effectively, and has also have better performances than the channel estimation algorithm based on DFT.%针对基于离散傅里叶变换（DFT）信道估计算法没有消除循环前缀长度以内噪声，本文提出了一种小波去噪与改进DFT相结合的正交频分复用（OFDM）系统信道估计算法。与传统的基于DFT信道估计算法相比，在误码率相同或者均方误差相同的条件下，该算法的信噪比（SNR）有了较大的提升。仿真结果表明，该算法能较好地减小信道所受噪声的影响，并能有效提高信道估计的精确度，其总体性能比基于DFT的信道估计算法更优。
Wavelets theory, algorithms, and applications
Montefusco, Laura
2014-01-01
Wavelets: Theory, Algorithms, and Applications is the fifth volume in the highly respected series, WAVELET ANALYSIS AND ITS APPLICATIONS. This volume shows why wavelet analysis has become a tool of choice infields ranging from image compression, to signal detection and analysis in electrical engineering and geophysics, to analysis of turbulent or intermittent processes. The 28 papers comprising this volume are organized into seven subject areas: multiresolution analysis, wavelet transforms, tools for time-frequency analysis, wavelets and fractals, numerical methods and algorithms, and applicat
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.
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.
Improved Real-time Denoising Method Based on Lifting Wavelet Transform
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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.
Directory of Open Access Journals (Sweden)
Sayadi Omid
2007-01-01
Full Text Available We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT, that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the -function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT. Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.
A New Wavelet Threshold Determination Method Considering Interscale Correlation in Signal Denoising
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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.
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.
Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach
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Kin Keung Lai
2012-04-01
Full Text Available In the increasingly globalized economy these days, the major crude oil markets worldwide are seeing higher level of integration, which results in higher level of dependency and transmission of risks among different markets. Thus the risk of the typical multi-asset crude oil portfolio is influenced by dynamic correlation among different assets, which has both normal and transient behaviors. This paper proposes a novel multivariate wavelet denoising based approach for estimating Portfolio Value at Risk (PVaR. The multivariate wavelet analysis is introduced to analyze the multi-scale behaviors of the correlation among different markets and the portfolio volatility behavior in the higher dimensional time scale domain. The heterogeneous data and noise behavior are addressed in the proposed multi-scale denoising based PVaR estimation algorithm, which also incorporatesthe mainstream time series to address other well known data features such as autocorrelation and volatility clustering. Empirical studies suggest that the proposed algorithm outperforms the benchmark ExponentialWeighted Moving Average (EWMA and DCC-GARCH model, in terms of conventional performance evaluation criteria for the model reliability.
Construction of a new adaptive wavelet network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.
ECG signals denoising using wavelet transform and independent component analysis
Liu, Manjin; Hui, Mei; Liu, Ming; Dong, Liquan; Zhao, Zhu; Zhao, Yuejin
2015-08-01
A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.
Impulse denoising using Hybrid Algorithm
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Ms.Arumugham Rajamani
2015-03-01
Full Text Available Many real time images facing a problem of salt and pepper noise contaminated,due to poor illumination and environmental factors. Many filters and algorithms are used to remove salt and pepper noise from the image, but it also removes image information. This paper proposes a new effective algorithm for diagnosing and removing salt and pepper noise is presented. The existing standard algorithms like Median Filter (MF, Weighted Median Filter (WMF, Standard Median Filter (SMF and so on, will yield poor performance particularly at high noise density. The suggested algorithm is compared with the above said standard algorithms using the metrics Mean Square Error (MSE and Peak Signal to Noise Ratio (PSNR value.The proposed algorithm exhibits more competitive performance results at all noise densities. The joint sorting and diagonal averaging algorithm has lower computational time,better quantitative results and improved qualitative result by a better visual appearance at all noise densities.
Energy Technology Data Exchange (ETDEWEB)
Bieleck, T.; Song, L.M.; Yau, S.S.T. [Univ. of Illinois, Chicago, IL (United States); Kwong, M.K. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
1995-07-01
The concepts of random wavelet transforms and discrete random wavelet transforms are introduced. It is shown that these transforms can lead to simultaneous compression and de-noising of signals that have been corrupted with fractional noises. Potential applications of algebraic geometric coding theory to encode the ensuing data are also discussed.
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.
A new adaptive algorithm for image denoising based on curvelet transform
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.
Serbes, Gorkem; Aydin, Nizamettin
2014-01-01
Quadrature signals are dual-channel signals obtained from the systems employing quadrature demodulation. Embolic Doppler ultrasound signals obtained from stroke-prone patients by using Doppler ultrasound systems are quadrature signals caused by emboli, which are particles bigger than red blood cells within circulatory system. Detection of emboli is an important step in diagnosing stroke. Most widely used parameter in detection of emboli is embolic signal-to-background signal ratio. Therefore, in order to increase this ratio, denoising techniques are employed in detection systems. Discrete wavelet transform has been used for denoising of embolic signals, but it lacks shift invariance property. Instead, dual-tree complex wavelet transform having near-shift invariance property can be used. However, it is computationally expensive as two wavelet trees are required. Recently proposed modified dual-tree complex wavelet transform, which reduces the computational complexity, can also be used. In this study, the denoising performance of this method is extensively evaluated and compared with the others by using simulated and real quadrature signals. The quantitative results demonstrated that the modified dual-tree-complex-wavelet-transform-based denoising outperforms the conventional discrete wavelet transform with the same level of computational complexity and exhibits almost equal performance to the dual-tree complex wavelet transform with almost half computational cost.
Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising
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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.
Fast DOA estimation using wavelet denoising on MIMO fading channel
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.
Modified Method for Denoising the Ultrasound Images by Wavelet Thresholding
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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.
A modified OSEM algorithm for PET reconstruction using wavelet processing.
Lee, Nam-Yong; Choi, Yong
2005-12-01
Ordered subset expectation-maximization (OSEM) method in positron emission tomography (PET) has been very popular recently. It is an iterative algorithm and provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. Due to the lack of smoothness in images in OSEM iterations, however, some type of inter-smoothing is required. For this purpose, the smoothing based on the convolution with the Gaussian kernel has been used in clinical PET practices. In this paper, we incorporated a robust wavelet de-noising method into OSEM iterations as an inter-smoothing tool. The proposed wavelet method is based on a hybrid use of the standard wavelet shrinkage and the robust wavelet shrinkage to have edge preserving and robust de-noising simultaneously. The performances of the proposed method were compared with those of the smoothing methods based on the convolution with Gaussian kernel using software phantoms, physical phantoms, and human PET studies. The results demonstrated that the proposed wavelet method provided better spatial resolution characteristic than the smoothing methods based on the Gaussian convolution, while having comparable performance in noise removal.
Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform.
Serbes, Gorkem; Aydin, Nizamettin
2010-01-01
Early and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB).
A cross-correlation based fiber optic white-light interferometry with wavelet transform denoising
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.
[A new wavelet image de-noising method based on new threshold function].
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.
Chambolle's Projection Algorithm for Total Variation Denoising
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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.
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.
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.
De-Noising SPECT Images from a Typical Collimator Using Wavelet Transform
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Farshid Babapour Mofrad
2009-12-01
Full Text Available Introduction: SPECT is a diagnostic imaging technique the main disadvantage of which is the existence of Poisson noise. So far, different methods have been used by scientists to improve SPECT images. The Wavelet Transform is a new method for de-noising which is widely used for noise reduction and quality enhancement of images. The purpose of this paper is evaluation of noise reduction in SPECT images by wavelet. Material and Methods: To calculate and simulate noise in images, it is common in nuclear medicine to use Monte Carlo techniques. The SIMIND software was used to simulate SPECT images in this research. The simulated and real images formed using the current typical (hexagonal collimator were de-noised by different types of wavelets. Results: The best type of wavelet was selected for SPECT images. The results demonstrated that the best type of wavelet in the simulated and real images increased Signal to Noise Ratio (SNR by 33% and 45% respectively. Also, Coefficient of Variation (CV decreased by 77% and 71% respectively, while Contrast of Recovery (CR was reduced by only 4% and 9% respectively. Conclusion: Comparing the results for real SPECT images in this paper with previously acquired results in real PET images, it can be concluded that the images of both nuclear medicine systems using Wavelet Transform differ in SNR and CR by only 5% and 7% respectively, and in CV by about 20%. Therefore, wavelet transform is applicable for nuclear medicine image de-noising.
Application and improvement of wavelet packet de-noising in satellite transponder
Institute of Scientific and Technical Information of China (English)
Yannian Lou; Chaojie Zhang; Xiaojun Jin; Zhonghe Jin
2015-01-01
The satel ite transponder is a widely used module in satel ite missions, and the most concerned issue is to reduce the noise of the transferred signal. Otherwise, the telemetry signal wil be pol uted by the noise contained in the transferred signal, and the additional power wil be consumed. Therefore, a method based on wavelet packet de-noising (WPD) is introduced. Compared with other techniques, there are two features making WPD more suit-able to be applied to satel ite transponders: one is the capability to deal with time-varying signals without any priori information of the input signals; the other is the capability to reduce the noise in band, even if the noise overlaps with signals in the frequency domain, which provides a great de-noising performance especial y for wideband signals. Besides, an oscil ation detector and an av-eraging filter are added to decrease the partial oscil ation caused by the thresholding process of WPD. Simulation results show that the proposed algorithm can reduce more noises and make less distortions of the signals than other techniques. In addition, up to 12 dB additional power consumption can be reduced at –10 dB signal-to-noise ratio (SNR).
Shi, Fei; Wang, Beibei; Selesnick, Ivan W.; Wang, Yao
2006-01-01
This paper introduces an anisotropic decomposition structure of a recently introduced 3-D dual-tree discrete wavelet transform (DDWT), and explores the applications for video denoising and coding. The 3-D DDWT is an attractive video representation because it isolates motion along different directions in separate subbands, and thus leads to sparse video decompositions. Our previous investigation shows that the 3-D DDWT, compared to the standard discrete wavelet transform (DWT), complies better with the statistical models based on sparse presumptions, and gives better visual and numerical results when used for statistical denoising algorithms. Our research on video compression also shows that even with 4:1 redundancy, the 3-D DDWT needs fewer coefficients to achieve the same coding quality (in PSNR) by applying the iterative projection-based noise shaping scheme proposed by Kingsbury. The proposed anisotropic DDWT extends the superiority of isotropic DDWT with more directional subbands without adding to the redundancy. Unlike the original 3-D DDWT which applies dyadic decomposition along all three directions and produces isotropic frequency spacing, it has a non-uniform tiling of the frequency space. By applying this structure, we can improve the denoising results, and the number of significant coefficients can be reduced further, which is beneficial for video coding.
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.
A de-noising algorithm to improve SNR of segmented gamma scanner for spectrum analysis
Energy Technology Data Exchange (ETDEWEB)
Li, Huailiang, E-mail: li-huai-liang@163.com [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); Tuo, Xianguo [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); State Key Laboratory of Geohazard Prevention & Geoenvironmental Protection, Chengdu University of Technology, Chengdu 610059 (China); Shi, Rui [State Key Laboratory of Geohazard Prevention & Geoenvironmental Protection, Chengdu University of Technology, Chengdu 610059 (China); Zhang, Jinzhao; Henderson, Mark Julian [Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010 (China); Courtois, Jérémie; Yan, Minhao [State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010 (China)
2016-05-11
An improved threshold shift-invariant wavelet transform de-noising algorithm for high-resolution gamma-ray spectroscopy is proposed to optimize the threshold function of wavelet transforms and reduce signal resulting from pseudo-Gibbs artificial fluctuations. This algorithm was applied to a segmented gamma scanning system with large samples in which high continuum levels caused by Compton scattering are routinely encountered. De-noising data from the gamma ray spectrum measured by segmented gamma scanning system with improved, shift-invariant and traditional wavelet transform algorithms were all evaluated. The improved wavelet transform method generated significantly enhanced performance of the figure of merit, the root mean square error, the peak area, and the sample attenuation correction in the segmented gamma scanning system assays. We also found that the gamma energy spectrum can be viewed as a low frequency signal as well as high frequency noise superposition by the spectrum analysis. Moreover, a smoothed spectrum can be appropriate for straightforward automated quantitative analysis.
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。
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.
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.
Directory of Open Access Journals (Sweden)
Szi-Wen Chen
2015-10-01
Full Text Available In this paper, a discrete wavelet transform (DWT based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan 40 nm standard cell library. The integrated circuit (IC synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.
Comparative Study of Image Denoising Algorithms in Digital Image Processing
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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.
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.
A Wavelet Phase Filtering Algorithm for Image Noise Reduction%图像噪声去除的小波相位滤波算法
Institute of Scientific and Technical Information of China (English)
赵瑞珍; 徐龙; 宋国乡
2001-01-01
Most of the wavelet denoising methods available are based on magnitudes. However,for the images with low SNR.the edges of the image m the wavelet domain are hidden in the noise. A wavelet phase filtering algorithm is presented in this paper, which is insensitive to the magnitude of image.
Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm.
Smith, L N; Olson, C C; Judd, K P; Nichols, J M
2012-06-10
Recent work has shown that tailored overcomplete dictionaries can provide a better image model than standard basis functions for a variety of image processing tasks. Here we propose a modified K-SVD dictionary learning algorithm designed to maintain the advantages of the original approach but with a focus on improved convergence. We then use the learned model to denoise infrared maritime imagery and compare the performance to the original K-SVD algorithm, several overcomplete "fixed" dictionaries, and a standard wavelet denoising algorithm. Results indicate the superiority of overcomplete representations and show that our tailored approach provides similar peak signal-to-noise ratios as the traditional K-SVD at roughly half the computational cost.
EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface
Institute of Scientific and Technical Information of China (English)
WU Ting; YAN Guo-zheng; YANG Bang-hua; SUN Hong
2007-01-01
Electroencephalogram (EEG) signal preprocessing is one of the most important techniques in brain computer interface (BCI). The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition. Wavelet transform is a method of multi-resolution time-frequency analysis, it can decompose the mixed signals which consist of different frequencies into different frequency band. EEG signal is analyzed and denoised using wavelet transform. Moreover, wavelet transform can be used for EEG feature extraction. The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features. The eigenvector for classification is obtained by combining the effective features from different channels. The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition, the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.
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.
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.
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.
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.
Random Modeling of Daily Rainfall and Runoff Using a Seasonal Model and Wavelet Denoising
Directory of Open Access Journals (Sweden)
Chien-ming Chou
2014-01-01
Full Text Available Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.
对偶树复小波在脑电消噪中的应用%Application of dual-tree complex wavelet transform in EEG denoising
Institute of Scientific and Technical Information of China (English)
姚家扬; 罗志增
2015-01-01
To solve information loss and frequency aliasing by discrete wavelet transform in the process of electroencephalogram (EEG)denoising,a new EEG signal denoising algorithm was pro-posed,which was based on dual-tree complex wavelet transform.The dual-tree complex wavelet transform was used to conduct a multilayered decomposition on the EEG inputted,so the real tree and the imaginary tree could be obtained,and the median threshold function was used to process the off-spring wavelet coefficients of the real tree and the imaginary tree,then the denoised wavelet was ob-tained by the method of inverse transformation.Simulation results reveal that the SNR and mean square error (MSE)of the proposed method are better than those of traditional discrete wavelet de-noising method,and the proposed method is more suitable for processing the weak EEG signal.%为解决脑电去噪过程中离散小波带来的信息丢失与频率混叠问题，提出了一种新型对偶树复小波去噪方法。用对偶树复小波对输入脑电信号（EEG）进行多层分解，得到实树部分与虚树部分，分别对实树部与虚树部各子代小波系数进行小波中值阈值处理，再逆变换得到去噪小波。仿真结果表明：该方法可以比传统离散小波去噪方法获得更好的信噪比与均方误差，因此更适合于处理微弱的脑电信号。
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.
Online Wavelet Denoising via a Moving Window%基于移动帧的在线小波去噪
Institute of Scientific and Technical Information of China (English)
夏睿; 孟科; 钱锋; 王振雷
2007-01-01
In this paper, shortcoming of traditional wavelet denoising in real-time signal processing is discussed, requirements of online denoising are considered, and a moving window is introduced into traditional wavelet transform. Using the moving window,an online wavelet denoising approach is proposed. Some problems of online denoising, such as border distortion and pseudo-Gibbs phenomena, are discussed. To solve these problems, window extension and window cycle spinning are also proposed. Different approaches are tested by the signal widely used in denoising domain. Both the visual results and the quantitative measures are presented to highlight the availability of the new approach.
Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method
Institute of Scientific and Technical Information of China (English)
LI Hong-kun; MA Xiao-jiang; WANG Zhen; ZHU Hong
2003-01-01
The local wave method is a very good time-frequency method for nonstationary vibration signal analysis. But the interfering noise has a big influence on the accuracy of time-frequency analysis. The wavelet packet de-noising method can eliminate the interference of noise and improve the signal-noise-ratio. This paper uses the local wave method to decompose the de-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally, an example of wavelet packet de-noising and a local wave time-frequency spectrum application of diesel engine surface vibration signal is put forward.
Majorization-minimization algorithms for wavelet-based image restoration.
Figueiredo, Mário A T; Bioucas-Dias, José M; Nowak, Robert D
2007-12-01
Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.
Application of wavelet transform de-noising in pole identification of a system
Institute of Scientific and Technical Information of China (English)
CHEN Shi-Guo; ZHANG Ruan-Yu; WANG Peng; LI Tai-Hua
2004-01-01
Due to the multiscale character of wavelet transform, the method of wavelet transform de-noising (WTDN) is introduced. The WTDN method neither requires extra limit of frequency range for the processed signal,nor needs a prior estimate of impulse response for an identified system, so it is especially suitable to de-noise the wide-band signal and the impulse response of a blind system. The numerical simulation results indicate that the WTDN method is reliable. The WTDN method was used to process the sampled data from a preamplifier coupled to a gas detector. The experimental results also show that the WTDN method can effectively improve the SNR of sampled data and enhance the accuracy in pole identification of the system.
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...
Srivastava, Madhur; Georgieva, Elka R; Freed, Jack H
2017-03-30
We adapt a new wavelet-transform-based method of denoising experimental signals to pulse-dipolar electron-spin resonance spectroscopy (PDS). We show that signal averaging times of the time-domain signals can be reduced by as much as 2 orders of magnitude, while retaining the fidelity of the underlying signals, in comparison with noiseless reference signals. We have achieved excellent signal recovery when the initial noisy signal has an SNR ≳ 3. This approach is robust and is expected to be applicable to other time-domain spectroscopies. In PDS, these time-domain signals representing the dipolar interaction between two electron spin labels are converted into their distance distribution functions P(r), usually by regularization methods such as Tikhonov regularization. The significant improvements achieved by using denoised signals for this regularization are described. We show that they yield P(r)'s with more accurate detail and yield clearer separations of respective distances, which is especially important when the P(r)'s are complex. Also, longer distance P(r)'s, requiring longer dipolar evolution times, become accessible after denoising. In comparison to standard wavelet denoising approaches, it is clearly shown that the new method (WavPDS) is superior.
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.
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.
Denoising of magnetotelluric signals by polarization analysis in the discrete wavelet domain
Carbonari, R.; D'Auria, L.; Di Maio, R.; Petrillo, Z.
2017-03-01
Magnetotellurics (MT) is one of the prominent geophysical methods for underground deep exploration and, thus, appropriate for applications to petroleum and geothermal research. However, it is not completely reliable when applied in areas characterized by intense urbanization, as the presence of cultural noise may significantly affect the MT impedance tensor estimates and, consequently, the apparent resistivity values that describe the electrical behaviour of the investigated buried structures. The development of denoising techniques of MT data is thus one of the main objectives to make magnetotellurics reliably even in urban or industrialized environments. In this work we propose an algorithm for filtering of MT data affected by temporally localized noise. It exploits the discrete wavelet transform (DWT) that, thanks to the possibility to operates in both time and frequency domain, allows to detect transient components of the MT signal, likely due to disturbances of anthropic nature. The implemented filter relies on the estimate of the ellipticity of the polarized MT wave. The application of the filter to synthetic and field MT data has proven its ability in detecting and removing cultural noise, thus providing apparent resistivity curves more smoothed than those obtained by using raw signals.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
Chaolong Zhang; Yigang He; Lifeng Yuan; Sheng Xiang; Jinping Wang
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 ...
Wavelet transform-based methods for denoising of Coulter counter signals
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.
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.)
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.
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.
Ren, Zhong; Liu, Guodong; Huang, Zhen
2014-10-01
Real-time monitoring of blood glucose concentration (BGC) is a great important procedure in controlling diabetes mellitus and preventing the complication for diabetic patients. Noninvasive measurement of BGC has already become a research hotspot because it can overcome the physical and psychological harm. Photoacoustic spectroscopy is a well-established, hybrid and alternative technique used to determine the BGC. According to the theory of photoacoustic technique, the blood is irradiated by plused laser with nano-second repeation time and micro-joule power, the photoacoustic singals contained the information of BGC are generated due to the thermal-elastic mechanism, then the BGC level can be interpreted from photoacoustic signal via the data analysis. But in practice, the time-resolved photoacoustic signals of BGC are polluted by the varities of noises, e.g., the interference of background sounds and multi-component of blood. The quality of photoacoustic signal of BGC directly impacts the precision of BGC measurement. So, an improved wavelet denoising method was proposed to eliminate the noises contained in BGC photoacoustic signals. To overcome the shortcoming of traditional wavelet threshold denoising, an improved dual-threshold wavelet function was proposed in this paper. Simulation experimental results illustrated that the denoising result of this improved wavelet method was better than that of traditional soft and hard threshold function. To varify the feasibility of this improved function, the actual photoacoustic BGC signals were test, the test reslut demonstrated that the signal-to-noises ratio(SNR) of the improved function increases about 40-80%, and its root-mean-square error (RMSE) decreases about 38.7-52.8%.
Matsuyama, Eri; Tsai, Du-Yih; Lee, Yongbum; Takahashi, Noriyuki
2013-01-01
The purpose of this study was to evaluate the performance of a conventional discrete wavelet transform (DWT) method and a modified undecimated discrete wavelet transform (M-UDWT) method applied to mammographic image denoising. Mutual information, mean square error, and signal to noise ratio were used as image quality measures of images processed by the two methods. We examined the performance of the two methods with visual perceptual evaluation. A two-tailed F test was used to measure statistical significance. The difference between the M-UDWT processed images and the conventional DWT-method processed images was statistically significant (P<0.01). The authors confirmed the superiority and effectiveness of the M-UDWT method. The results of this study suggest the M-UDWT method may provide better image quality as compared to the conventional DWT.
Parallel transformation of K-SVD solar image denoising algorithm
Liang, Youwen; Tian, Yu; Li, Mei
2017-02-01
The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform.
Patel, Ameera X; Bullmore, Edward T
2016-11-15
Connectome mapping using techniques such as functional magnetic resonance imaging (fMRI) has become a focus of systems neuroscience. There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series. One key statistic for any time series is its (effective) degrees of freedom, df, which will generally be less than the number of time points (or nominal degrees of freedom, N). If we know the df, then probabilistic inference on other fMRI statistics, such as the correlation between two voxel or regional time series, is feasible. However, we currently lack good estimators of df in fMRI time series, especially after the degrees of freedom of the "raw" data have been modified substantially by denoising algorithms for head movement. Here, we used a wavelet-based method both to denoise fMRI data and to estimate the (effective) df of the denoised process. We show that seed voxel correlations corrected for locally variable df could be tested for false positive connectivity with better control over Type I error and greater specificity of anatomical mapping than probabilistic connectivity maps using the nominal degrees of freedom. We also show that wavelet despiked statistics can be used to estimate all pairwise correlations between a set of regional nodes, assign a P value to each edge, and then iteratively add edges to the graph in order of increasing P. These probabilistically thresholded graphs are likely more robust to regional variation in head movement effects than comparable graphs constructed by thresholding correlations. Finally, we show that time-windowed estimates of df can be used for probabilistic connectivity testing or dynamic network analysis so that apparent changes in the functional connectome are appropriately corrected for the effects of transient noise bursts. Wavelet despiking is both an algorithm for fMRI time series denoising and an estimator of the (effective) df of denoised
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.
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.
Abibullaev, Berdakh; An, Jinung
2012-12-01
Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.
Tehrani, Kayvan Forouhesh; Mortensen, Luke J.; Kner, Peter
2016-03-01
Wavefront sensorless schemes for correction of aberrations induced by biological specimens require a time invariant property of an image as a measure of fitness. Image intensity cannot be used as a metric for Single Molecule Localization (SML) microscopy because the intensity of blinking fluorophores follows exponential statistics. Therefore a robust intensity-independent metric is required. We previously reported a Fourier Metric (FM) that is relatively intensity independent. The Fourier metric has been successfully tested on two machine learning algorithms, a Genetic Algorithm and Particle Swarm Optimization, for wavefront correction about 50 μm deep inside the Central Nervous System (CNS) of Drosophila. However, since the spatial frequencies that need to be optimized fall into regions of the Optical Transfer Function (OTF) that are more susceptible to noise, adding a level of denoising can improve performance. Here we present wavelet-based approaches to lower the noise level and produce a more consistent metric. We compare performance of different wavelets such as Daubechies, Bi-Orthogonal, and reverse Bi-orthogonal of different degrees and orders for pre-processing of images.
An Adaptive Inpainting Algorithm Based on DCT Induced Wavelet Regularization
2013-01-01
applications. We view the rows of a discrete cosine transform matrix as the filters associated with a multiresolution analysis. Non-decimated wavelet ...a redundant system which is formed by a set of transforms such as the discrete cosine transform, wavelets , framelets, and curvelets. The missing...vol. 93, pp. 273–299, 1965. [33] Q. Lian, L. Shen, Y. Xu, and L. Yang, “Filters of wavelets on invariant sets for image denoising ,” Applicable
Comparison of fast discrete wavelet transform algorithms
Institute of Scientific and Technical Information of China (English)
MENG Shu-ping; TIAN Feng-chun; XU Xin
2005-01-01
This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short-length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.
New Algorithm For Calculating Wavelet Transforms
Directory of Open Access Journals (Sweden)
Piotr Lipinski
2009-04-01
Full Text Available In this article we introduce a new algorithm for computing Discrete Wavelet Transforms (DWT. The algorithm aims at reducing the number of multiplications, required to compute a DWT. The algorithm is general and can be used to compute a variety of wavelet transform (Daubechies and CDF. Here we focus on CDF 9/7 filters, which are used in JPEG2000 compression standard. We show that the algorithm outperforms convolution-based and lifting-based algorithms in terms of number of multiplications.
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.
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.
Wavelet based denoising of power quality events for characterization
African Journals Online (AJOL)
user
efficient algorithm, known as multi-resolution signal decomposition technique. ... function serving as high pass filter with filter coefficients g(n), generates the ... at different resolution levels, the standard deviation and/or mean value and the.
Directory of Open Access Journals (Sweden)
Li Song
2010-04-01
Full Text Available Abstract Background Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results We developed a novel discrete wavelet transform (DWT and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP, a commonly used open source proteomics analysis pipeline. Conclusions We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.
Novel wavelet threshold denoising method in axle press-fit zone ultrasonic detection
Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai
2017-02-01
Axles are important part of railway locomotives and vehicles. Periodic ultrasonic inspection of axles can effectively detect and monitor axle fatigue cracks. However, in the axle press-fit zone, the complex interface contact condition reduces the signal-noise ratio (SNR). Therefore, the probability of false positives and false negatives increases. In this work, a novel wavelet threshold function is created to remove noise and suppress press-fit interface echoes in axle ultrasonic defect detection. The novel wavelet threshold function with two variables is designed to ensure the precision of optimum searching process. Based on the positive correlation between the correlation coefficient and SNR and with the experiment phenomenon that the defect and the press-fit interface echo have different axle-circumferential correlation characteristics, a discrete optimum searching process for two undetermined variables in novel wavelet threshold function is conducted. The performance of the proposed method is assessed by comparing it with traditional threshold methods using real data. The statistic results of the amplitude and the peak SNR of defect echoes show that the proposed wavelet threshold denoising method not only maintains the amplitude of defect echoes but also has a higher peak SNR.
Xu, Fan; Wang, Yuanqing
2015-11-01
Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.
Zou, Ling; Zhang, Yingchun; Yang, Laurence T; Zhou, Renlai
2010-02-01
The authors have developed a new approach by combining the wavelet denoising and principal component analysis methods to reduce the number of required trials for efficient extraction of brain evoked-related potentials (ERPs). Evoked-related potentials were initially extracted using wavelet denoising to enhance the signal-to-noise ratio of raw EEG measurements. Principal components of ERPs accounting for 80% of the total variance were extracted as part of the subspace of the ERPs. Finally, the ERPs were reconstructed from the selected principal components. Computer simulation results showed that the combined approach provided estimations with higher signal-to-noise ratio and lower root mean squared error than each of them alone. The authors further tested this proposed approach in single-trial ERPs extraction during an emotional process and brain responses analysis to emotional stimuli. The experimental results also demonstrated the effectiveness of this combined approach in ERPs extraction and further supported the view that emotional stimuli are processed more intensely.
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.
Super-resolution image restoration algorithms based on orthogonal discrete wavelet transform
Liu, Yang-yang; Jin, Wei-qi
2005-02-01
Several new super-resolution image restoration algorithms based on orthogonal discrete wavelet transform are proposed, by using orthogonal discrete wavelet transform and generalized cross validation ,and combining with Luck-Richardson super-resolution image restoration algorithm (LR) and Luck-Richardson algorithm based on Poisson-Markov model (MPML). Orthogonal discrete wavelet transform analyzed in both space and frequency domain has the capability of indicating local features of a signal, and concentrating the signal power to a few coefficients in wavelet transform domain. After an original image is "Symlets" orthogonal discrete wavelet transformed, an asymptotically optimal threshold is determined by minimizing generalized cross validation, and high frequency subbands in each decomposition level are denoised with soft threshold processes to converge respectively to those with maximum signal-noise-ratio, when the method is incorporated with existed super-resolution image algorithms, details of original image, especially of those with low signal-noise-ratio, could be well recovered. Single operation wavelet LR algorithm(SWLR),single operation wavelet MPML algorithm(SW-MPML) and MPML algorithm based on single operation and wavelet transform (MPML- SW) are some operative algorithms proposed based on the method. According to the processing results to simulating and practical images , because of the only one operation, under the guarantee of rapid and effective restoration processing, in comparison with LR and MPML, all the proposed algorithms could retain image details better, and be more suitable to low signal-noise-ratio images, They could also reduce operation time for up to hundreds times of iteratives, as well as, avoid the iterative operation of self-adaptive parameters in MPML, improve operating speed and precision. They are practical and instantaneous to some extent in the field of low signal-noise-ratio image restoration.
Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage
2014-01-01
The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT co...
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
A Real-Time Wavelet-Domain Video Denoising Implementation in FPGA
Directory of Open Access Journals (Sweden)
Pižurica Aleksandra
2006-01-01
Full Text Available The use of field-programmable gate arrays (FPGAs for digital signal processing (DSP has increased with the introduction of dedicated multipliers, which allow the implementation of complex algorithms. This architecture is especially effective for data-intensive applications with extremes in data throughput. Recent studies prove that the FPGAs offer better solutions for real-time multiresolution video processing than any available processor, DSP or general-purpose. FPGA design of critically sampled discrete wavelet transforms has been thoroughly studied in literature over recent years. Much less research was done towards FPGA design of overcomplete wavelet transforms and advanced wavelet-domain video processing algorithms. This paper describes the parallel implementation of an advanced wavelet-domain noise filtering algorithm, which uses a nondecimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The implemented arithmetic is decentralized and distributed over two FPGAs. The standard composite television video stream is digitalized and used as a source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real-time video processing.
A Real-Time Wavelet-Domain Video Denoising Implementation in FPGA
Directory of Open Access Journals (Sweden)
Wilfried Philips
2006-07-01
Full Text Available The use of field-programmable gate arrays (FPGAs for digital signal processing (DSP has increased with the introduction of dedicated multipliers, which allow the implementation of complex algorithms. This architecture is especially effective for data-intensive applications with extremes in data throughput. Recent studies prove that the FPGAs offer better solutions for real-time multiresolution video processing than any available processor, DSP or general-purpose. FPGA design of critically sampled discrete wavelet transforms has been thoroughly studied in literature over recent years. Much less research was done towards FPGA design of overcomplete wavelet transforms and advanced wavelet-domain video processing algorithms. This paper describes the parallel implementation of an advanced wavelet-domain noise filtering algorithm, which uses a nondecimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The implemented arithmetic is decentralized and distributed over two FPGAs. The standard composite television video stream is digitalized and used as a source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real-time video processing.
Zielinski, B.; Patorski, K.
2008-12-01
The aim of this paper is to analyze the accuracy of 2D fringe pattern denoising performed by two chosen methods using quasi-1D two-arm spin filter and 2D Discrete Wavelet Transform (DWT) signal decomposition and thresholding. The ultimate aim of this comparison is to estimate which algorithm is better suited for high-accuracy interferometric measurements. In spite of the fact that both algorithms are designed to minimize possible fringe blur and distortion, the evaluation of errors introduced by each algorithm is essential for proper estimation of their performance.
Numerical Algorithms Based on Biorthogonal Wavelets
Ponenti, Pj.; Liandrat, J.
1996-01-01
Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.
Multi-threshold de-noising of electrical imaging logging data based on the wavelet packet transform
Xie, Fang; Xiao, Chengwen; Liu, Ruilin; Zhang, Lili
2017-08-01
A key problem of effectiveness evaluation for fractured-vuggy carbonatite reservoir is how to accurately extract fracture and vug information from electrical imaging logging data. Drill bits quaked during drilling and resulted in rugged surfaces of borehole walls and thus conductivity fluctuations in electrical imaging logging data. The occurrence of the conductivity fluctuations (formation background noise) directly affects the fracture/vug information extraction and reservoir effectiveness evaluation. We present a multi-threshold de-noising method based on wavelet packet transform to eliminate the influence of rugged borehole walls. The noise is present as fluctuations in button-electrode conductivity curves and as pockmarked responses in electrical imaging logging static images. The noise has responses in various scales and frequency ranges and has low conductivity compared with fractures or vugs. Our de-noising method is to decompose the data into coefficients with wavelet packet transform on a quadratic spline basis, then shrink high-frequency wavelet packet coefficients in different resolutions with minimax threshold and hard-threshold function, and finally reconstruct the thresholded coefficients. We use electrical imaging logging data collected from fractured-vuggy Ordovician carbonatite reservoir in Tarim Basin to verify the validity of the multi-threshold de-noising method. Segmentation results and extracted parameters are shown as well to prove the effectiveness of the de-noising procedure.
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.
Denoising of gravitational wave signals via dictionary learning algorithms
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.
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.
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
Directory of Open Access Journals (Sweden)
Guxi Wang
2015-01-01
Full Text Available Seismic data processing is an important aspect to improve the signal to noise ratio. The main work of this paper is to combine the characteristics of seismic data, using wavelet transform method, to eliminate and control such random noise, aiming to improve the signal to noise ratio and the technical methods used in large data systems, so that there can be better promotion and application. In recent years, prestack data denoising of all-digital three-dimensional seismic data is the key to data processing. Contrapose the characteristics of all-digital three-dimensional seismic data, and, on the basis of previous studies, a new threshold function is proposed. Comparing between conventional hard threshold and soft threshold, this function not only is easy to compute, but also has excellent mathematical properties and a clear physical meaning. The simulation results proved that this method can well remove the random noise. Using this threshold function in actual seismic processing of unconventional lithologic gas reservoir with low porosity, low permeability, low abundance, and strong heterogeneity, the results show that the denoising method can availably improve seismic processing effects and enhance the signal to noise ratio (SNR.
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.
Directory of Open Access Journals (Sweden)
Md. Zahangir Alam
2011-01-01
Full Text Available The paper proposes a novel technique for reducing noise in M-ary signal transmission through wireless fading channel using wavelet denoising that play the key role. The paper also explains that the conventional threshold-based technique is not capable of denoising M-ary quadrature amplitude modulated (M-QAM signals having multilevel wavelet coefficients through wireless fading channels. A detailed step by step wavelet decomposition and reconstruction processes are discussed here to transform a signal function into wavelet coefficients using simulation software like MATLAB. A 16-QAM modulated symbol through a Rician fading channel is weighted by a control variable of complex form to force the mean of each detail coefficient except low frequency component to zero to enhance noiseless property. The bit error rate (BER performance of the simulation results are furnished to show the effectiveness of the proposed technique. The root mean square of the deviation of the reconstruct signal from the original signal is used to express the effectiveness of the proposed technique. The traditional denoising provides very high value (above 90% of the percentage root mean square difference (PDR and the proposed technique provides only 10% PDR value for the symbol through a noisy channel. The result of the simulation study reveals that the BER performance can be increased using an appropriate control variable to force the mean of each detail coefficient to zero.
RESEARCH OF PROBLEMS ON REALIZING DIRECT ALGORITHM OF WAVELET TRANSFORM
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Direct algorithm of wavelet transform (WT) is the numerical algorithm obtained from the integral formula of WT by directly digitization.Some problems on realizing the algorithm are studied.Some conclusions on the direct algorithm of discrete wavelet transform (DWT), such as discrete convolution operation formula of wavelet coefficients and wavelet components, sampling principle and technology to wavelets, deciding method for scale range of wavelets, measures to solve edge effect problem, etc, are obtained.The realization of direct algorithm of continuous wavelet transform (CWT) is also studied.The computing cost of direct algorithm and Mallat algorithm of DWT are still studied, and the computing formulae are obtained.These works are beneficial to deeply understand WT and Mallat algorithm.Examples in the end show that direct algorithm can also be applied widely.
Cheng, Lizhi; Luo, Yong; Chen, Bo
2014-01-01
This book could be divided into two parts i.e. fundamental wavelet transform theory and method and some important applications of wavelet transform. In the first part, as preliminary knowledge, the Fourier analysis, inner product space, the characteristics of Haar functions, and concepts of multi-resolution analysis, are introduced followed by a description on how to construct wavelet functions both multi-band and multi wavelets, and finally introduces the design of integer wavelets via lifting schemes and its application to integer transform algorithm. In the second part, many applications are discussed in the field of image and signal processing by introducing other wavelet variants such as complex wavelets, ridgelets, and curvelets. Important application examples include image compression, image denoising/restoration, image enhancement, digital watermarking, numerical solution of partial differential equations, and solving ill-conditioned Toeplitz system. The book is intended for senior undergraduate stude...
Institute of Scientific and Technical Information of China (English)
刘锡祥; 杨燕; 黄永江; 宋清
2016-01-01
Double-vector attitude determination algorithm in inertial frame takes two gravitational apparent motion vectors as non-collinear vectors. Although this method solve the traditional algorithm’s problem that the information is susceptible to angular motion disturbance on swinging base, it still needs accurate latitude information to participate in alignment calculation. Aiming to fulfill the alignment for strapdown inertial navigation system without aided latitude information, a self-alignment method with three gravitational apparent motion vectors is designed. In this method, the alignment problem is attributed to solving the attitude matrix between current navigation frame and initial body frame and is solved with vector operation. Simulation results indicate that those random noises in the accelerator will be projected in gravitation apparent motion vectors and decrease the alignment accuracy, and even cause alignment failure when with large noise. For denoising, the daubechies (db4) wavelet is introduced to decompose gravitational apparent motions with 5 layers, and three denoised apparent motion vectors are selected to participate in the alignment. Simulation results indicate that the db4 owns excellent denoising effects and the alignment method with three apparent motion vectors and db4 in inertial frame can fulfill the alignment without aided latitude information.%基于惯性系的双矢量定姿方法选择惯性系中的两个重力视运动向量作为不共线矢量，解决了传统双矢量定姿方法在晃动基座条件下易受载体角运动干扰而无法实现对准的问题，但该方法仍需要精确的地理纬度信息以参与对准计算。针对未知纬度条件下的SINS抗晃动自对准问题，提出了一种基于重力视运动的三矢量自对准方法。该方法将初始对准问题归结为求解当前时刻导航系相对于初始时刻载体系的姿态矩阵问题，并利用矢量运算进行求解，仿真结果表
Directory of Open Access Journals (Sweden)
Mr. Arun Kumar
2014-08-01
Full Text Available De-noising of the raw vibration signal is essential requirement to improve the accuracy and efficiency of any fault diagnosis of method. In many cases the noise signal is even stronger than the actual signal, so it is important to have such system in which noise elimination can be done effectively, there are many time domain and frequency domain methods are already available, where use of wavelet as time-frequency domain method in the field of de-noising the vibration signal is relatively new, it gives multi resolution analysis in both is time-frequency domain. In this paper various conventional thresholding methods based on discrete wavelet transform are compared with adaptive thresholding method and Penalized thresholding method for the de-noising of vibration signal of rotating machine. Signal to noise ratio (SNR, root mean square error (RMSE in between de-noised signal with original signal are used as an indicator for selecting the effective thesholding method.
Directory of Open Access Journals (Sweden)
Dib Djamel Eddine
2012-02-01
Full Text Available In this paper, we study the Doppler effect on a GPS(Global Positioning System on board of an observation satellite that receives information on a carrier wave L1 frequency 1575.42 MHz .We simulated GPS signal acquisition. This allowed us to see the behavior of this type of receiver in AWGN channel (AWGN and we define a method to reduce the Doppler Effect in the tracking loop which is wavelet de-noising technique.
Image compression algorithm using wavelet transform
Cadena, Luis; Cadena, Franklin; Simonov, Konstantin; Zotin, Alexander; Okhotnikov, Grigory
2016-09-01
Within the multi-resolution analysis, the study of the image compression algorithm using the Haar wavelet has been performed. We have studied the dependence of the image quality on the compression ratio. Also, the variation of the compression level of the studied image has been obtained. It is shown that the compression ratio in the range of 8-10 is optimal for environmental monitoring. Under these conditions the compression level is in the range of 1.7 - 4.2, depending on the type of images. It is shown that the algorithm used is more convenient and has more advantages than Winrar. The Haar wavelet algorithm has improved the method of signal and image processing.
A Remark on the Mallat Pyramidal Algorithm of Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
无
1997-01-01
The exact relationships between the lenthgs of scale sequences and wavelet sequences in the Mallat pyramidal algorithm for computing wavelet trans-form coefficients are obtained,and the maximum possible scale of arbitrary discrete signal is derived.
Application of Sample Entropy Based LMD-TFPF De-Noising Algorithm for the Gear Transmission System
Directory of Open Access Journals (Sweden)
Shaohui Ning
2016-11-01
Full Text Available This paper investigates an improved noise reduction method and its application on gearbox vibration signal de-noising. A hybrid de-noising algorithm based on local mean decomposition (LMD, sample entropy (SE, and time-frequency peak filtering (TFPF is proposed. TFPF is a classical filter method in the time-frequency domain. However, there is a contradiction in TFPF, i.e., a good preservation for signal amplitude, but poor random noise reduction results might be obtained by selecting a short window length, whereas a serious attenuation for signal amplitude, but effective random noise reduction might be obtained by selecting a long window length. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, LMD and SE are adopted to improve TFPF. Firstly, the original signal is decomposed into PFs by LMD, and the SE value of each product function (PF is calculated in order to classify the numerous PFs into the useful component, mixed component, and the noise component; then short-window TFPF is employed for the useful component, long-window TFPF is employed for the mixed component, and the noise component is removed; finally, the final signal is obtained after reconstruction. The gearbox vibration signals are employed to verify the proposed algorithm, and the comparison results show that the proposed SE-LMD-TFPF has the best de-noising results compared to traditional wavelet and TFPF method.
Denoising of Rotating Blade Vibration Signal by Wavelet Transform%旋转叶片振动信号的小波变换去噪处理
Institute of Scientific and Technical Information of China (English)
刘瑾; 黄健; 叶德超; 李刚; 金鑫杰
2016-01-01
To ensure the high precision measurement of vibration displacement , it is necessary to denoise vibration signal in the rotating blade tip timing vibration system , where the blades usually work in big noise environment .Using wavelet transform denoising method which has the local time-frequency charac-teristics, the simulation model of the blade tip timing vibration system was established .According to the simulation signal characteristics, this paper optimized the parameters of the wavelet base , the threshold estimation and the decomposition scale, and effectively filtered out the broadband white noise .Compared with the traditional filtering method and Savitzky-Golay (SG) smoothing algorithm, the wavelet transform denoising method has obvious advantages in denoising effect and reducing of vibration displacement meas -urement error ,which has good application value in practical engineering .%在旋转叶片叶尖定时测振系统中，叶片多处于大噪声工作环境，为了保障振动位移的高精度测量，需有效解决叶片振动信号的去噪问题。提出采用具有时频局部分析特性的小波变换方法对振动信号进行去噪，建立了叶尖定时测振系统的仿真模型。针对仿真信号的特征，对小波变换的小波基、阈值估计及小波分解尺度进行了参数优化，实现了宽频白噪声的有效滤除。并与传统的滤波和Savitzky-Golay（ SG）平滑算法进行对比，结果表明小波变换去噪方法在去噪效果和减小振动位移测量误差上均具有明显优势，有较好的实际工程应用价值。
Institute of Scientific and Technical Information of China (English)
栾某德; 刘涤尘; 廖清芬; 董超; 欧阳利平
2012-01-01
Based on the combination of continuous wavelet transform （CWT） with singular value decomposition （SVD）, a new algorithm to identify oscillation frequency of signal, extract mode information and denoise signal by raising SVD of wavelet coefficient is proposed. The condition that under high noise level or closely spaced mode of noise, the wavelet ridges are unsharp and even the frequency is hard to extract due to the aliasing and intersection of wavelet ridges can be overcome by the proposed method, and the frequencies of oscillation modes in different orders can be identified according to frequency vectors of the raised SVD of wavelet coefficients. Meanwhile the wavelet energy coefficient is chosen to identify the dominant oscillation mode, and signal denoising is performed by use of wavelet soft-thresholding denoising and restructured matrix after the SVD of wavelet coefficient. CWT can be used to deal with time-varying low-frequency oscillation signals containing time-varying oscillation mode, and the identification accuracy of mode parameters is high. Both effectiveness and applicability of the proposed algorithm are verified by simulation results.%提出了一种基于连续小波变换（continuouswalelettranSform，CwT）和奇异值分解（Singularvaluedecomposition，SVD）相结合的提升小波系数SVD辨识信号振荡频率和模式信息提取及信号去噪的新方法。克服了噪声较大或者密集模态时，小波脊线不清晰甚至会出现混叠和交叉难以提取频率的情况，根据提升的小波系数奇异值分解频率向量识别各阶振荡模式的频率。同时选用小波能量系数来识别主导振荡模式，用小波软阈值去噪和SVD分解后矩阵重构来进行信号去噪。CWT可以处理含时变振荡模式的低频振荡信号，且对模式参数具有较高的辨识精度。仿真算例验证了算法的有效性和适用性。
Zielinski, B.; Patorski, K.
2010-06-01
The aim of this paper is to analyze 2D fringe pattern denoising performed by two chosen methods based on quasi-1D two-arm spin filter and 2D discrete wavelet transform (DWT) signal decomposition and thresholding. The ultimate aim of this comparison is to estimate which algorithm is better suited for high-accuracy measurements by phase shifting interferometry (PSI) with the phase step evaluation using the lattice site approach. The spin filtering method proposed by Yu et al. (1994) was designed to minimize possible fringe blur and distortion. The 2D DWT also presents such features due to a lossless nature of the signal wavelet decomposition. To compare both methods, a special 2D histogram introduced by Gutman and Weber (1998) is used to evaluate intensity errors introduced by each of the presented algorithms.
Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform
Institute of Scientific and Technical Information of China (English)
Zhang Xiaofei; Xu Dazhuan
2005-01-01
An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.
Mass spectrometry cancer data classification using wavelets and genetic algorithm.
Nguyen, Thanh; Nahavandi, Saeid; Creighton, Douglas; Khosravi, Abbas
2015-12-21
This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.
A New Denoising System for SONAR Images
Directory of Open Access Journals (Sweden)
Alexandru Isar
2009-01-01
Full Text Available The SONAR images are perturbed by speckle noise. The use of speckle reduction filters is necessary to optimize the image exploitation procedures. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and textural information of the scene. Shift-invariance associated with good directional selectivity is important for the use of a wavelet transform (WT in many fields of image processing. Generally, complex wavelet transforms, for example, the Double Tree Complex Wavelet Transform (DT-CWT have these useful properties. In this paper, we propose the use of the DT-CWT in association with Maximum A Posteriori (MAP filters. Such systems carry out different quality denoising in regions with different homogeneity degree. We propose a solution for the reduction of this unwanted effect based on diversity enhancement. The corresponding denoising algorithm is simple and fast. Some simulation results prove the performance obtained.
A Novel Algorithm for Robust Audio Watermarking in Wavelet Domain
Institute of Scientific and Technical Information of China (English)
FU Yu; WANG Bao-bao; LI Chun-ru; QUAN Ning-qiang
2004-01-01
A novel algorithm for digital audio watermarking in wavelet domain is proposed. First,an original audio signal is decomposed by discrete wavelet transform at three levels. Then, a discrete watermark is embedded into the coefficients of its intermediate frequencies. Finally, the watermarked audio signal is obtained by wavelet reconstruction. The proposed algorithm makes good use of the multiresolution characteristics of wavelet transform. The original audio signal is not needed when detecting the watermark correlatively. Simulation results show that the algorithm is inaudible and robust to noise, filtering and resampling.
A fast optimization transfer algorithm for image inpainting in wavelet domains.
Chan, Raymond H; Wen, You-Wei; Yip, Andy M
2009-07-01
A wavelet inpainting problem refers to the problem of filling in missing wavelet coefficients in an image. A variational approach was used by Chan et al. The resulting functional was minimized by the gradient descent method. In this paper, we use an optimization transfer technique which involves replacing their univariate functional by a bivariate functional by adding an auxiliary variable. Our bivariate functional can be minimized easily by alternating minimization: for the auxiliary variable, the minimum has a closed form solution, and for the original variable, the minimization problem can be formulated as a classical total variation (TV) denoising problem and, hence, can be solved efficiently using a dual formulation. We show that our bivariate functional is equivalent to the original univariate functional. We also show that our alternating minimization is convergent. Numerical results show that the proposed algorithm is very efficient and outperforms that of Chan et al.
Directory of Open Access Journals (Sweden)
Hongguang Liu
2016-12-01
Full Text Available Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI 300 futures in the emerging China Financial Futures Exchange (CFFEX market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.
2016-01-01
The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis. PMID:27248692
DENOISING AND HARMONIC DETECTION USING NONORTHOGONAL WAVELET PACKETS IN INDUSTRIAL APPLICATIONS
Institute of Scientific and Technical Information of China (English)
P. MERCORELLI
2007-01-01
New industrial applications call for new methods and new ideas in signal analysis. Wavelet packets are new tools in industrial applications and they have just recently appeared in projects and patents. In training neural networks, for the sake of dimensionality and of ratio of time, compact information is needed. This paper deals with simultaneous noise suppression and signal compression of quasi-harmonic signals. A quasi-harmonic signal is a signal with one dominant harmonic and some more sub harmonics in superposition. Such signals often occur in rail vehicle systems, in vhich noisy signals are present. Typically, they are signals which come from rail overhead power lines and are generated by intermodulation phenomena and radio interferences. An important task is to monitor and recognize them. This paper proposes an algorithm to differentiate discrete signals from their noisy observations using a library of nonorthonormal bases. The algorithm combines the shrinkagetechnique and techniques in regression analysis using Shannon Entropy function and Cross Entropy function to select the best discernable bases. Cosine and sine wavelet bases in wavelet packets are used.The algorithm is totally general and can be used in many industrial applications. The effectiveness of the proposed method consists of using as few as possible samples of the measured signal and in the meantime highlighting the difference between the noise and the desired signal. The problem is a difficult one, but well posed. In fact, compression reduces the level of the measured noise and undesired signals but introduces the well known compression noise. The goal is to extract a coherent signal from the measured signal which will be "well represented" by suitable waveforms and a noisy signal or incoherent signal which cannot be "compressed well" by the waveforms. Recursive residual iterations with cosine and sine bases allow the extraction of elements of the required signal and the noise. The algorithm
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.
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.
Chouakri, S. A.; Djaafri, O.; Taleb-Ahmed, A.
2013-08-01
We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly.
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.
Institute of Scientific and Technical Information of China (English)
曹世超; 张国勋
2012-01-01
为改善电能质量信号的去噪效果,提出一种基于双密度小波变换的自适应电能质量信号去噪算法.双密度小波变换具有近似的平移不变性,能更准确地描述信号的真实特征.而双变量收缩函数充分考虑小波系数的层内层间关系,对小波系数采用结合局部方差估计的双变量收缩函数进行去噪处理,并用收缩后的小波系数重构信号.实验结果表明:该算法在有效滤除噪声的同时,能够更好地保留电能质量信号的特征信息,使去噪信号的视觉信息有较大改善.%In order to improve the quality of the de-noised power quality signal, an efficient adaptive power quality signal de-noising algorithm based on the Double-Density Discrete Wavelet Transform(DD DWT) is proposed. DD DWT has approximately shift invariance and can accurately describe the real characteristics of signals. Meanwhile, the Bivariate Shrinkage Function (BSF) considers the relationship of the inter-level and intra-level coefficients. So, BSF with local variance estimation is adopted to processing wavelet coefficients. Then the signals are synthesized using the wavelet coefficients processed. The experimental results indicate that, the proposed algorithm can remove the noise more efficiently and keep original power quality signal characters, and that the visual quality of the denoised signal is improved.
Improving the performance of the prony method using a wavelet domain filter for MRI denoising.
Jaramillo, Rodney; Lentini, Marianela; Paluszny, Marco
2014-01-01
The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T 2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek's algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T 2 MR images, and the filter is applied to each image before using the variant of the Prony method.
Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising
Directory of Open Access Journals (Sweden)
Rodney Jaramillo
2014-01-01
Full Text Available The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek’s algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T2 MR images, and the filter is applied to each image before using the variant of the Prony method.
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.
A denoising algorithm for projection measurements in cone-beam computed tomography.
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.
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
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。
Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network
Institute of Scientific and Technical Information of China (English)
WANG Zhenfei; ZHAI Guangqun; WANG Nengchao
2006-01-01
An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.
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.
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.
一种改进的小波阈值去噪算法%An improved method for wavelet threshold denoising
Institute of Scientific and Technical Information of China (English)
张岩; 杨智刚; 魏泰
2015-01-01
For the difficulty of selecting threshold value in the traditional wavelet threshold denoising method,a new type of wavelet-empirical model decomposition ( EMD) -singular value decomposition ( SVD) is proposed. An original signal is decomposed by wavelet transform and a group of detail signals are obtained, however, heavy noise wavelet coefficients are not simply set to zero by wavelet threshold any more, whereas these heavy noise wavelet coefficients are used to EMD and SVD difference spectrum decomposing and extracts the weak signal. Finally reconstructs the single. The result indicates that the denoising effects are obvious and smooth in heavy noise surrounding.%针对传统小波阈值去噪算法中阈值选取困难，提出一种新的小波-EMD-SVD差分谱组合模式。对原始信号做小波分解得到一系列细节信号，不再通过小波阈值将强噪声小波系数简单地置零，而是对其进行EMD-SVD差分谱微弱信号提取，最后进行重构。实验结果表明，该算法对强噪声环境下信号的去噪效果明显且平稳。
An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm
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.
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.
a Universal De-Noising Algorithm for Ground-Based LIDAR Signal
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.
Improved zerotree coding algorithm for wavelet image compression
Chen, Jun; Li, Yunsong; Wu, Chengke
2000-12-01
A listless minimum zerotree coding algorithm based on the fast lifting wavelet transform with lower memory requirement and higher compression performance is presented in this paper. Most state-of-the-art image compression techniques based on wavelet coefficients, such as EZW and SPIHT, exploit the dependency between the subbands in a wavelet transformed image. We propose a minimum zerotree of wavelet coefficients which exploits the dependency not only between the coarser and the finer subbands but also within the lowest frequency subband. And a ne listless significance map coding algorithm based on the minimum zerotree, using new flag maps and new scanning order different form Wen-Kuo Lin et al. LZC, is also proposed. A comparison reveals that the PSNR results of LMZC are higher than those of LZC, and the compression performance of LMZC outperforms that of SPIHT in terms of hard implementation.
Fast Wavelet Transform Algorithms With Low Memory Requirements
Directory of Open Access Journals (Sweden)
Maya Babuji
2010-06-01
Full Text Available In this paper, a new algorithm to efficiently compute the two-dimensional wavelet transform is presented. This algorithm aims at low memory consumption and reduced complexity, meeting these requirements by means of line-by-line processing. In this proposal,we use recursion to automatically place the order in which the wavelet transform is computed. This way, we solve some synchronization problems that have not been tackled byprevious proposals. Furthermore, unlike other similar proposals, our proposal can be straightforwardly implemented from the algorithm description. To this end, a general algorithm is given which is further detailed to allow its implementation with a simple filterbank or using the more efficient lifting scheme. We also include a new fast run-length encoder to be used along with the proposed wavelet transform for fast image compression and reduced memory consumption.
Institute of Scientific and Technical Information of China (English)
黄令勇; 宋力杰; 王琰; 任雅奇; 刘毅锟; 宁德阳
2012-01-01
针对Compass电离层二阶改正存在的问题,提出利用小波包分析算法对观测值进行消噪,再进行三频二阶改正的方法.利用Compass仿真数据的试验表明,小波消噪算法能够大大削弱观测噪声,有利于提高Compass三频二阶改正的精度,增强其可行性和有效性.%For taking the problem in Compass two order ionospheric correction, the algorithm of wavelet package de-noising is used to weaken the noises before ionospheric delay correction. Finally, the experiment is made by Compass emulational data and the results show that wavelet packet de-noising method can decrease the observation errors and enhance the feasibility and validity of triple-frequency of two order correction greatly.
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.
Xian, Yong-Li; Dai, Yun; Gao, Chun-Ming; Du, Rui
2017-01-01
Noninvasive measurement of hemoglobin oxygen saturation (SO2) in retinal vessels is based on spectrophotometry and spectral absorption characteristics of tissue. Retinal images at 570 and 600 nm are simultaneously captured by dual-wavelength retinal oximetry based on fundus camera. SO2 is finally measured after vessel segmentation, image registration, and calculation of optical density ratio of two images. However, image noise can dramatically affect subsequent image processing and SO2 calculation accuracy. The aforementioned problem remains to be addressed. The purpose of this study was to improve image quality and SO2 calculation accuracy by noise analysis and denoising algorithm for dual-wavelength images. First, noise parameters were estimated by mixed Poisson-Gaussian (MPG) noise model. Second, an MPG denoising algorithm which we called variance stabilizing transform (VST) + dual-domain image denoising (DDID) was proposed based on VST and improved dual-domain filter. The results show that VST + DDID is able to effectively remove MPG noise and preserve image edge details. VST + DDID is better than VST + block-matching and three-dimensional filtering, especially in preserving low-contrast details. The following simulation and analysis indicate that MPG noise in the retinal images can lead to erroneously low measurement for SO2, and the denoised images can provide more accurate grayscale values for retinal oximetry.
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.
A Study on System Identification Using Wavelet Transformation
Energy Technology Data Exchange (ETDEWEB)
Baek, Wook Jin; Han, Jeong Woo; Kang, Sung Ju [Department of Chemical Engineering, Chonnam National University, Kwangju (Korea); Chung, Chang Bock [Faculty of Applied Chemical Engineering, Chonnam National University, Kwangju (Korea)
2001-04-01
The wavelet transformation, which was developed in order to overcome the defects of traditional Fourier transformation, is applied to many fields of study in various ways-for example, de-noising, data compression and mathematic applications such as solving partial differential equations, etc. De-noising is one of the wavelet transformation and has been studied by many researchers. The effect of de-noising depends upon the shrinkage function and the method of choosing the threshold value for the function. The objective of this work is to analyze the results of applying various threshold algorithms according to characteristics for signals and noise level. By applying the de-noising to the system identification, we compared the performances of signals which went through the de-noising process with those of signals with out de-noising. 28 refs., 12 figs., 10 tabs.
Adaptive wavelet transform algorithm for lossy image compression
Pogrebnyak, Oleksiy B.; Ramirez, Pablo M.; Acevedo Mosqueda, Marco Antonio
2004-11-01
A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response. It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non-adaptive transform, which introduces ringing artifacts. The designed algorithm can be used for lossy image compression and in the noise suppression applications.
[A fast non-local means algorithm for denoising of computed tomography images].
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.
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.
De-Noising Ultrasound Images of Colon Tumors Using Daubechies Wavelet Transform
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.
Automatic Image Registration Algorithm Based on Wavelet Transform
Institute of Scientific and Technical Information of China (English)
LIU Qiong; NI Guo-qiang
2006-01-01
An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the least-squares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.
Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm
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W. M. Bukhari
2011-01-01
Full Text Available Problem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of wavelet transform was to distribute the signal energy with the change of time in different frequency bands. This will showed the characteristic of the signals since energy was an important physical variable in signal analysis. The EOG signals were captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features used to determine the characteristic of eye movement waveform. This technique adopted because it was a non-invasive, inexpensive and accurate. The new technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200. The recorded data was composed of an eye movement toward four directions, i.e., downward, upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT with energy algorithm and by comparing the energy distribution with the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to display the different percentages of energy for each wavelet coefficient towards different movement. Conclusion: From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding scale such as leftward with scale 6 (8- 16Hz, rightward with scale 8 (2-4Hz, downward with scale 9 (1-2Hz and upward with level 7 (4-8Hz. Statistically, the results in this study indicate that there are 93% (averages significance differences in the extracted features of wavelet Scalogram analysis.
Denoising Worm Artifacts of Elastogram Using 2-D Wavelet Shrinkage%使用二维小波收缩法去除弹性成像蠕虫噪声
Institute of Scientific and Technical Information of China (English)
崔少国; 刘东权
2011-01-01
This paper proposes a technique to denoise the worm artifacts of elastogram using 2-D wavelet shrinkage denoising method. Firstly, strain estimate matrix including worm artifacts was decomposed to 3 levels by 2-D discrete wavelet transform with Sym8 wavelet function, and the thresholds were obtained using Birge-Massart algorithm. Secondly, all the high frequency coefficients on different levels were quantized by using hard threshold and soft threshold function. Finally, the strain estimate matrix was reconstructed by using the 3rd layer low frequency coefficients and other layer quantized high frequency coefficients. The simulation results illustrated that the present technique could efficiently denoise the worm artifacts, enhance the elastogram performance indices, such as elasto-graphic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe), and could increase the correlation coefficient between the denoised elastogram and the ideal elastogram. In comparison with 2-D low-pass filtering, it could also obtain the higher elastographic SNRe and CNRe, and have clearer hard lesion edge. In addition, the results demonstrated that the proposed technique could suppress worm artifacts of elastograms for various applied strains. This work showed that the 2-D wavelet shrinkage denoising could efficiently denoise the worm artifacts of elastogram and enhance the performance of elastogram.%本文研究使用二维小波收缩去噪法去除弹性成像过程中产生的蠕虫噪声.先使用Sym8小波函数对含有蠕虫噪声的应变估计值矩阵进行3级二维离散小波分解,并使用Birgé-Massart算法获取二维小波变换的域值;然后分别使用硬域值函数和软域值函数对各尺度的水平方向、垂直方向、对角方向的高频系数进行量化;最后将第3层低频系数和各层被量化后的高频系数进行二维小波重构产生去噪后的弹性图像.仿真结果显示,提出的技术有效去除了弹性成
小波系数扩散的多步图像去噪方法%More Steps De-noising Method of Wavelet Coefficients Diffusion
Institute of Scientific and Technical Information of China (English)
刘晨华; 冯象初
2013-01-01
为了研究小波和偏微分方程在图像去噪方面的相关性,对小波阈值去噪过程进行了分析,得到了基于小波变换的偏微分方程关系式.利用小波变换的模代替梯度算子的模检测边缘,能较好地实现对图像特征的平滑.在此基础上进一步研究了该关系式的解法,提出了小波系数扩散的多步图像去噪方法.该方法通过对小波系数归一化,把得到的状态权通过各向异性扩散后作用在原小波系数上,采用多步方法实现了图像去噪,达到了既保护边缘又去除噪声的目的.数值实验结果表明:该方法使峰值信噪比平均提高约1.9dB,视觉效果也有较大提高.%For the sake of researching the relations between wavelet and PDE,the process is researched that image noise is removed by making use of discrete wavelet threshold transform.The expression form of PDE that based on wavelet transform is then obtained.The magnitude of wavelet wansform is substituted for the module of the gradient operator,which realizes image smooth according to the characteristics of the image.The solution is researched for the expression form of PDE,a new more steps de-noising method of wavelet coefficients diffusion is proposed.The wavelet coefficients are normalized which can gain the corresponding state weights.The new state weights are obtained that the corresponding state weights are denoised by anisotropic diffusion.The new wavelet coefficients are gained when the new state weights act on the original wavelet coefficients.The process of de-noising is achieved through the method of more steps for the solution of PDE in the end.It can achieve the purpose of keeping the detailed edges and resisting image noises at the same time.Experiments show there is an increase of about 1.9 dB on average in the peak signal to noise ratio and a remarkable improvement on the visual effects.
Steady-state sweep visual evoked potential processing denoised by wavelet transform
Weiderpass, Heinar A.; Yamamoto, Jorge F.; Salomão, Solange R.; Berezovsky, Adriana; Pereira, Josenilson M.; Sacai, Paula Y.; de Oliveira, José P.; Costa, Marcio A.; Burattini, Marcelo N.
2008-03-01
Visually evoked potential (VEP) is a very small electrical signal originated in the visual cortex in response to periodic visual stimulation. Sweep-VEP is a modified VEP procedure used to measure grating visual acuity in non-verbal and preverbal patients. This biopotential is buried in a large amount of electroencephalographic (EEG) noise and movement related artifact. The signal-to-noise ratio (SNR) plays a dominant role in determining both systematic and statistic errors. The purpose of this study is to present a method based on wavelet transform technique for filtering and extracting steady-state sweep-VEP. Counter-phase sine-wave luminance gratings modulated at 6 Hz were used as stimuli to determine sweep-VEP grating acuity thresholds. The amplitude and phase of the second-harmonic (12 Hz) pattern reversal response were analyzed using the fast Fourier transform after the wavelet filtering. The wavelet transform method was used to decompose the VEP signal into wavelet coefficients by a discrete wavelet analysis to determine which coefficients yield significant activity at the corresponding frequency. In a subsequent step only significant coefficients were considered and the remaining was set to zero allowing a reconstruction of the VEP signal. This procedure resulted in filtering out other frequencies that were considered noise. Numerical simulations and analyses of human VEP data showed that this method has provided higher SNR when compared with the classical recursive least squares (RLS) method. An additional advantage was a more appropriate phase analysis showing more realistic second-harmonic amplitude value during phase brake.
An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm
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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.
Spherical 3D isotropic wavelets
Lanusse, F.; Rassat, A.; Starck, J.-L.
2012-04-01
Context. Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D spherical Fourier-Bessel (SFB) analysis in spherical coordinates is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. Aims: The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. Methods: We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. (2006). We also present a new fast discrete spherical Fourier-Bessel transform (DSFBT) based on both a discrete Bessel transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and find we can successfully remove noise without much loss to the large scale structure. Results: We have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel DSFBT. We illustrate its potential use for denoising using a toy model. All the algorithms presented in this paper are available for download as a public code called MRS3D at http://jstarck.free.fr/mrs3d.html
Heart sound denoising using local projection and discrete wavelet transform%局部投影和离散小波变换在心音信号去噪中的应用
Institute of Scientific and Technical Information of China (English)
梁庆真; 郭兴明; 袁志会
2014-01-01
In view of the nonlinear characteristics of heart sound signals,a heart sound denoising algorithm which uses local projection based on adaptive estimation of noise level combined with discrete wavelet threshold was proposed.It can reconstruct the signal accurately and can retain the effective characteristics of weak signal.The numerical simulation results of the Lorenz sequence show that the method can effectively filter out the noise,and the signal to noise ratio and mean square error are superior to those when the local projection method or the discrete wavelet threshold denoising method is used alone.Comparing the results by using different algorithms according to the largest Lyapunov indices of the signal before and after denoising,it is concluded that the method presented can filter out noise and can still better retain the characteristics of the original signal.The denoising practice on actual measured heart sounds further shows the effectiveness of the method.%针对心音信号非线性的特点，提出噪声水平自适应估计的局部投影与离散小波阈值相结合的去噪方法，该算法既能得到精确的重构信号又能保留微弱信号的有效特征。Lorenz序列数值仿真结果表明，该方法可以有效地抑制噪声，其信噪比和均方误差均优于局部投影去噪和离散小波阈值去噪；对比不同算法去噪前后信号的最大Lyapunov指数，得出该方法能很好地保留原始信号的非线性特征。对实测心音信号的降噪研究，进一步表明了该方法的有效性。
Mishra, C.; Samantaray, A. K.; Chakraborty, G.
2016-05-01
Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data1
Gao, Zong-li; Ye, Wei-lin; Zheng, Chuan-tao; Wang, Yi-ding
2014-07-01
A novel wavelet denoising (WD) assisted wavelength modulation technique is proposed for improving near-infrared detection performance on methane concentration based on tunable diode laser absorption spectroscopy (TDLAS). Due to the ability of multi-level analytical resolutions both in time- and frequency-domains, the noise contained in the differential signal is greatly suppressed. Sensor mechanical part, optical part and electrical part are integrated, and a portable detection device is finally developed. Theory and formulations of the WD-assisted wavelength modulation technique are presented, and experiments are carried out to prove the normal function on the extraction of the second harmonic (2f) signal from severely polluted differential signal by using the technique. By virtue of WD's suppression on noises, the sensing characteristics on CH4 concentration are improved, and the limit of detection (LOD) is decreased from 4×10-6 (without WD processing) to 10-6. The proposed technique can also be used for the measurement on the concentration of other gases with corresponding near-infrared distributed feedback lasers.
A multiscale products technique for denoising of DNA capillary electrophoresis signals
Gao, Qingwei; Lu, Yixiang; Sun, Dong; Zhang, Dexiang
2013-06-01
Since noise degrades the accuracy and precision of DNA capillary electrophoresis (CE) analysis, signal denoising is thus important to facilitate the postprocessing of CE data. In this paper, a new denoising algorithm based on dyadic wavelet transform using multiscale products is applied for the removal of the noise in the DNA CE signal. The adjacent scale wavelet coefficients are first multiplied to amplify the significant features of the CE signal while diluting noise. Then, noise is suppressed by applying a multiscale threshold to the multiscale products instead of directly to the wavelet coefficients. Finally, the noise-free CE signal is recovered from the thresholded coefficients by using inverse dyadic wavelet transform. We compare the performance of the proposed algorithm with other denoising methods applied to the synthetic CE and real CE signals. Experimental results show that the new scheme achieves better removal of noise while preserving the shape of peaks corresponding to the analytes in the sample.
Reducing Ultrasonic Signal Noise by Algorithms based on Wavelet Thresholding
Directory of Open Access Journals (Sweden)
M. Kreidl
2002-01-01
Full Text Available Traditional techniques for reducing ultrasonic signal noise are based on the optimum frequency of an acoustic wave, ultrasonic probe construction and low-noise electronic circuits. This paper describes signal processing methods for noise suppression using a wavelet transform. Computer simulations of the proposed testing algorithms are presented.
Analysis of a wavelet-based robust hash algorithm
Meixner, Albert; Uhl, Andreas
2004-06-01
This paper paper is a quantitative evaluation of a wavelet-based, robust authentication hashing algorithm. Based on the results of a series of robustness and tampering sensitivity tests, we describepossible shortcomings and propose variousmodifications to the algorithm to improve its performance. The second part of the paper describes and attack against the scheme. It allows an attacker to modify a tampered image, such that it's hash value closely matches the hash value of the original.
Adaptive wavelet transform algorithm for image compression applications
Pogrebnyak, Oleksiy B.; Manrique Ramirez, Pablo
2003-11-01
A new algorithm of locally adaptive wavelet transform is presented. The algorithm implements the integer-to-integer lifting scheme. It performs an adaptation of the wavelet function at the prediction stage to the local image data activity. The proposed algorithm is based on the generalized framework for the lifting scheme that permits to obtain easily different wavelet coefficients in the case of the (N~,N) lifting. It is proposed to perform the hard switching between (2, 4) and (4, 4) lifting filter outputs according to an estimate of the local data activity. When the data activity is high, i.e., in the vicinity of edges, the (4, 4) lifting is performed. Otherwise, in the plain areas, the (2,4) decomposition coefficients are calculated. The calculations are rather simples that permit the implementation of the designed algorithm in fixed point DSP processors. The proposed adaptive transform possesses the perfect restoration of the processed data and possesses good energy compactation. The designed algorithm was tested on different images. The proposed adaptive transform algorithm can be used for image/signal lossless compression.
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.
A chaos-based robust wavelet-domain watermarking algorithm
Energy Technology Data Exchange (ETDEWEB)
Zhao Dawei E-mail: davidzhaodw@hotmail.com; Chen Guanrong; Liu Wenbo
2004-10-01
In this paper, a chaos-based watermarking algorithm is developed in the wavelet domain for still images. The wavelet transform is commonly applied for watermarking, where the whole image is transformed in the frequency domain. In contrast to this conventional approach, we apply the wavelet transform only locally. We transform the subimage, which is extracted from the original image, in the frequency domain by using DWT and then embed the chaotic watermark into part of the subband coefficients. As usual, the watermark is detected by computing the correlation between the watermarked coefficients and the watermarking signal, where the watermarking threshold is chosen according to the Neyman-Pearson criterion based on some statistical assumptions. Watermark detection is accomplished without using the original image. Simulation results show that we can gain high fidelity and high robustness, especially under the typical attack of geometric operations.
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.
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.
Bitenc, M.; Kieffer, D. S.; Khoshelham, K.
2016-06-01
Terrestrial Laser Scanning (TLS) is a well-known remote sensing tool that enables precise 3D acquisition of surface morphology from distances of a few meters to a few kilometres. The morphological representations obtained are important in engineering geology and rock mechanics, where surface morphology details are of particular interest in rock stability problems and engineering construction. The actual size of the discernible surface detail depends on the instrument range error (noise effect) and effective data resolution (smoothing effect). Range error can be (partly) removed by applying a denoising method. Based on the positive results from previous studies, two denoising methods, namely 2D wavelet transform (WT) and non-local mean (NLM), are tested here, with the goal of obtaining roughness estimations that are suitable in the context of rock engineering practice. Both methods are applied in two variants: conventional Discrete WT (DWT) and Stationary WT (SWT), classic NLM (NLM) and probabilistic NLM (PNLM). The noise effect and denoising performance are studied in relation to the TLS effective data resolution. Analyses are performed on the reference data acquired by a highly precise Advanced TOpometric Sensor (ATOS) on a 20x30 cm rock joint sample. Roughness ratio is computed by comparing the noisy and denoised surfaces to the original ATOS surface. The roughness ratio indicates the success of all denoising methods. Besides, it shows that SWT oversmoothes the surface and the performance of the DWT, NLM and PNLM vary with the noise level and data resolution. The noise effect becomes less prominent when data resolution decreases.
Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals
Directory of Open Access Journals (Sweden)
Peng Zhang
2016-01-01
Full Text Available Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement.
Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames.
Pad, Pedram; Uhlmann, Virginie; Unser, Michael
2016-05-01
A crucial component of steerable wavelets is the radial profile of the generating function in the frequency domain. In this paper, we present an infinite-dimensional optimization scheme that helps us find the optimal profile for a given criterion over the space of tight frames. We consider two classes of criteria that measure the localization of the wavelet. The first class specifies the spatial localization of the wavelet profile, and the second that of the resulting wavelet coefficients. From these metrics and the proposed algorithm, we construct tight wavelet frames that are optimally localized and provide their analytical expression. In particular, one of the considered criterion helps us finding back the popular Simoncelli wavelet profile. Finally, the investigation of local orientation estimation, image reconstruction from detected contours in the wavelet domain, and denoising indicate that optimizing wavelet localization improves the performance of steerable wavelets, since our new wavelets outperform the traditional ones.
A Steganographic Method Based on Integer Wavelet Transform & Genatic Algorithm
Directory of Open Access Journals (Sweden)
Preeti Arora
2014-05-01
Full Text Available The proposed system presents a novel approach of building a secure data hiding technique of steganography using inverse wavelet transform along with Genetic algorithm. The prominent focus of the proposed work is to develop RS-analysis proof design with higest imperceptibility. Optimal Pixal Adjustment process is also adopted to minimize the difference error between the input cover image and the embedded-image and in order to maximize the hiding capacity with low distortions respectively. The analysis is done for mapping function, PSNR, image histogram, and parameter of RS analysis. The simulation results highlights that the proposed security measure basically gives better and optimal results in comparison to prior research work conducted using wavelets and genetic algorithm.
Remote Sensing Image Resolution Enlargement Algorithm Based on Wavelet Transformation
Directory of Open Access Journals (Sweden)
Samiul Azam
2014-05-01
Full Text Available In this paper, we present a new image resolution enhancement algorithm based on cycle spinning and stationary wavelet subband padding. The proposed technique or algorithm uses stationary wavelet transformation (SWT to decompose the low resolution (LR image into frequency subbands. All these frequency subbands are interpolated using either bicubic or lanczos interpolation, and these interpolated subbands are put into inverse SWT process for generating intermediate high resolution (HR image. Finally, cycle spinning (CS is applied on this intermediate high resolution image for reducing blocking artifacts, followed by, traditional Laplacian sharpening filter is used to make the generated high resolution image sharper. This new technique has been tested on several satellite images. Experimental result shows that the proposed technique outperforms the conventional and the state-of-the-art techniques in terms of peak signal to noise ratio, root mean square error, entropy, as well as, visual perspective.
A New Shape-Coding Algorithm by Using Wavelet Transform
Institute of Scientific and Technical Information of China (English)
石旭利; 张兆杨
2003-01-01
In this paper, we propose a new shape-coding algorithm called wavelet-based shape coding (WBSC). Performing wavelet transform on the orientation of original planar curve gives the corners called corner-1 points and end of arcs that belong to the original curve. Each arc is represented by a broken line and the corners called corner-2 points of the broken line are extracted. A polygonal approximation of a contour is an ordered list of corner-1 points, ends of arcs and corner-2 points which are extracted by using the above algorithm. All of the points are called polygonal vertices which will be compressed by our adaptive arithmetic encoding. Experimental results show that our method reduces code bits by about 26% compared with the context-based arithmetic encoding (CAE) of MPEG-4, and the subjective quality of the reconstructed shape is better than that of CAE at the same Dn.
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.
Pereira, Danilo Cesar; Ramos, Rodrigo Pereira; do Nascimento, Marcelo Zanchetta
2014-04-01
In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Institute of Scientific and Technical Information of China (English)
Han Wenhua; Que Peiwen
2006-01-01
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects,and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
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
Directory of Open Access Journals (Sweden)
Tran Thai Trung
2014-10-01
Full Text Available Since the penetration level of wind energy is continuously increasing, the negative impact caused by the fluctuation of wind power output needs to be carefully managed. This paper proposes a novel real-time coordinated control algorithm based on a wavelet transform to mitigate both short-term and long-term fluctuations by using a hybrid energy storage system (HESS. The short-term fluctuation is eliminated by using an electric double-layer capacitor (EDLC, while the wind-HESS system output is kept constant during each 10-min period by a Ni-MH battery (NB. State-of-charge (SOC control strategies for both EDLC and NB are proposed to maintain the SOC level of storage within safe operating limits. A ramp rate limitation (RRL requirement is also considered in the proposed algorithm. The effectiveness of the proposed algorithm has been tested by using real time simulation. The simulation model of the wind-HESS system is developed in the real-time digital simulator (RTDS/RSCAD environment. The proposed algorithm is also implemented as a user defined model of the RSCAD. The simulation results demonstrate that the HESS with the proposed control algorithm can indeed assist in dealing with the variation of wind power generation. Moreover, the proposed method shows better performance in smoothing out the fluctuation and managing the SOC of battery and EDLC than the simple moving average (SMA based method.
Spherical 3D Isotropic Wavelets
Lanusse, F; Starck, J -L
2011-01-01
Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis in is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the Fourier-Bessel decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. 2006. We also present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large...
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.
Bioucas-Dias, José M
2006-04-01
Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys, and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(N log N) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.
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.
Institute of Scientific and Technical Information of China (English)
黄建招; 谢建; 李锋; 任建华
2012-01-01
提出基于离散平稳小波的改进自适应降噪方法,并将该方法应用于压力信号消噪,取得了优于传统小波消噪的效果.方法利用平稳小波的冗余特性,解决了传统二进小波变换降噪方法在奇异点存在振荡效应的不足,同时将尺度系数的噪声与各层噪声强度不同纳入分析.利用噪声强度估计各分解层阈值,对尺度和小波系数同时进行自适应降噪.将该方法应用于压力信号消噪,并与传统离散二进小波进行比较,证明了方法的可行性和有效性.%An improved self-adaptive de-noising method based on discrete stationary wavelet transform is put forward. The method is used for pressure signal de-noising and get better effect than the traditional discrete binary wavelet de-noising. In this method, the discrete stationary wavelet transform is used to solve the oscillation effects of singular points in signal de-noising by the discrete binary wavelet transform,and the detail coefficients noise and different noise intensity in different level are taken into consideration. The detail and approximation coefficients in each decomposition level are self-adaptive denoised using the thresholds, which are estimated by the noise intensity accordingly. The pressure signal de-noisong experiment is carried out compared with the traditional discrete binary wavelet transform,the feasibility and validity of the mehtod are proved.
Institute of Scientific and Technical Information of China (English)
Changjiang Zhang; Xiaodong Wang; Haoran Zhang
2005-01-01
A new contrast enhancement algorithm for image is proposed employing wavelet neural network (WNN)and stationary wavelet transform (SWT). Incomplete Beta transform (IBT) is used to enhance the global contrast for image. In order to avoid the expensive time for traditional contrast enhancement algorithms,which search optimal gray transform parameters in the whole gray transform parameter space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameter space is given respectively according to different contrast types,which shrinks the parameter space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm (SA) so as to obtain optimal gray transform parameters. Thus the searching direction and selection of initial values of simulated annealing is guided by the new parameter space. In order to calculate IBT in the whole image, a kind of WNN is proposed to approximate the IBT. Having enhanced the global contrast to input image, discrete SWT is done to the image which has been processed by previous global enhancement method, local contrast enhancement is implemented by a kind of nonlinear operator in the high frequency sub-band images of each decomposition level respectively. Experimental results show that the new algorithm is able to adaptively enhance the global contrast for the original image while it also extrudes the detail of the targets in the original image well. The computation complexity for the new algorithm is O(MN) log(MN), where M and N are width and height of the original image, respectively.
A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising
DEFF Research Database (Denmark)
Kærgaard, Kevin; Jensen, Søren Hjøllund; Puthusserypady, Sadasivan
2016-01-01
Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of theheart. These signals, however, are often obscured by artifacts/noises from various sources and mini-mization of these artifacts is of paramount importance for detecting anomalies. This paper presents......), named the Wavelet NN (WNN)) for denoising the ECG signals. These methods arecompared to the conventional EMD (C-EMD), C-EEMD, EEMD-LMS as well as the DWT thresholding(DWT-Th) based methods through extensive simulation studies on real as well as noise corrupted ECGsignals. Results clearly show...
Institute of Scientific and Technical Information of China (English)
朱俊敏; 张潇; 王旌阳; 吴粤北
2009-01-01
在数字化时代,音频的转录或录制都会引入噪音,但是历史音频保存和音频资料处理需要纯净的音频信号,因此音频降噪研究有着重要的现实意义.首先介绍了二进小波和奇异性指数,并阐述了尺度跟踪和模极大值重构等理论,在Mallat工作的基础上,提出了一种基于小波滤波的音频降噪方法.该方法首先引入补偿因子削减二进小波变换对系数造成的影响,并计算带噪音频的小波系数和模极大值;然后基于信号和噪声奇异指数不同的特点,结合阈值降噪和尺度跟踪理论,采用层间相关搜索去除噪声的模极大值;最后利用交替投影算法,重建音频信号.用该方法处理带click和hiss噪声的音频信号,跟小波阈值方法和小波包方法相比,能达到较好的听觉效果和信噪比.同时观察信号的波形图及模极大值演示图,发现该方法都表现出优异的降噪效果.%Audio recording or transcription inevitably brings in noise, so audio denoising is crucial for data processing and preservation. There are many existing techniques with respect to audio denoising based on wavelet transformation. An improved algorithm was provided based on three kinds of techniques: dyadic wavelet, scale tracking theory and modulus maximum theory. The novelties of the algorithm lie in the following: the compensation factors is introduced in order to reduce the influence of scale discretization; the interlayer correlation searching is used to eliminate noise according to the modulus maximum; and the original signal is reconstructed by using alternating projection algorithm. As an attempt, the algorithm was adopted to process audio signals with click and hiss noise, and better results were achieved, comparing with the wavelet threshold and wavelet packet algorithms in terms of comfort degree of hearing sense. By inspection with signal oscillograms and by display of modulus maxima it is verified the algorithm reduces
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.
Wang, Zhengzi; Ren, Zhong; Liu, Guodong
2016-10-01
In this paper, the wavelet threshold denoising method was used into the filtered back-projection algorithm of imaging reconstruction. To overcome the drawbacks of the traditional soft- and hard-threshold functions, a modified wavelet threshold function was proposed. The modified wavelet threshold function has two threshold values and two variants. To verify the feasibility of the modified wavelet threshold function, the standard test experiments were performed by using the software platform of MATLAB. Experimental results show that the filtered back-projection reconstruction algorithm based on the modified wavelet threshold function has better reconstruction effect because of more flexible advantage.
Higher-density dyadic wavelet transform and its application
Qin, Yi; Tang, Baoping; Wang, Jiaxu
2010-04-01
This paper proposes a higher-density dyadic wavelet transform with two generators, whose corresponding wavelet filters are band-pass and high-pass. The wavelet coefficients at each scale in this case have the same length as the signal. This leads to a new redundant dyadic wavelet transform, which is strictly shift invariant and further increases the sampling in the time dimension. We describe the definition of higher-density dyadic wavelet transform, and discuss the condition of perfect reconstruction of the signal from its wavelet coefficients. The fast implementation algorithm for the proposed transform is given as well. Compared with the higher-density discrete wavelet transform, the proposed transform is shift invariant. Applications into signal denoising indicate that the proposed wavelet transform has better denoising performance than other commonly used wavelet transforms. In the end, various typical wavelet transforms are applied to analyze the vibration signals of two faulty roller bearings, the results show that the proposed wavelet transform can more effectively extract the fault characteristics of the roller bearings than the other wavelet transforms.
Db复小波在超高频局部放电测量中的应用%Application of Complex Daubechies Wavelet in UHF Partial Discharge Measurements
Institute of Scientific and Technical Information of China (English)
谢颜斌; 唐炬; 张晓星
2008-01-01
On-line partial discharge (PD) detection still remains a very challenging task because of the strong electromagnetic interferences. In this paper, a new method of de-noising, using complex Daubechies wavelet (CDW) transform, has been proposed. It is a relatively recent enhancement to the real-valued wavelet transform because of tow important properties, which are nearly shift-invariant and availability of phase information. Those properties give CDW transform superiority over other real-valued wavelet transform, and then the construction algorithm of CDW is introduced in detail. Secondly, based on the real threshold algorithm of real-valued wavelet transform, complex threshold algorithm is devised. This algorithm take the different characteristics of real part and imaginary part of complex wavelet coefficients into account, it modifies the real and imaginary parts of complex wavelet coefficients respectively. Thirdly, to obtain a real de-noised signal, new combined information series is devised. By applying different combination of real part and imaginary part of de-noised complex signal, a real de-noised signal can be restored with higher peak signal-to-noise ratio (PSNR) and less distortion of original signals. Finally, On-site applications of extracting PD signals from noisy background by the optimal de-noising scheme based on CDW are illustrated. The on-site experimental results show that the optimal de-noising scheme is an effective way to suppress white noise in PD measurement.
Adaptive inpainting algorithm based on DCT induced wavelet regularization.
Li, Yan-Ran; Shen, Lixin; Suter, Bruce W
2013-02-01
In this paper, we propose an image inpainting optimization model whose objective function is a smoothed l(1) norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting.
Research on Wavelet-Based Algorithm for Image Contrast Enhancement
Institute of Scientific and Technical Information of China (English)
Wu Ying-qian; Du Pei-jun; Shi Peng-fei
2004-01-01
A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches.
An iterative denoising system based on Wiener filtering with application to biomedical images
Lahmiri, Salim
2017-05-01
Biomedical image denoising systems are important for accurate clinical diagnosis. The purpose of this study is to present a simple and effective iterative multistep image denoising system based on Wiener filtering (WF) where the denoised image from one stage is the input to the next stage. The denoising process stops when a particular condition measured by image energy is adaptively achieved. The proposed iterative system is tested on real clinical images and performance is measured by the well-known peak-signal-to-noise-ratio (PSNR) statistic. Experimental results showed that the proposed iterative system outperforms conventional image denoising algorithms; including wavelet packet (WP), fourth order partial differential equation (FOPDE), nonlocal Euclidean means (NLEM), first order local statistics (FOLS), and single Wiener filter used as baseline model. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively while edges and texture characteristics are preserved.
A nonlinear filtering algorithm for denoising HR(S)TEM micrographs
Energy Technology Data Exchange (ETDEWEB)
Du, Hongchu, E-mail: h.du@fz-juelich.de [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons, Jülich Research Centre, Jülich, 52425 (Germany); Central Facility for Electron Microscopy (GFE), RWTH Aachen University, Aachen 52074 (Germany); Peter Grünberg Institute, Jülich Research Centre, Jülich 52425 (Germany)
2015-04-15
Noise reduction of micrographs is often an essential task in high resolution (scanning) transmission electron microscopy (HR(S)TEM) either for a higher visual quality or for a more accurate quantification. Since HR(S)TEM studies are often aimed at resolving periodic atomistic columns and their non-periodic deviation at defects, it is important to develop a noise reduction algorithm that can simultaneously handle both periodic and non-periodic features properly. In this work, a nonlinear filtering algorithm is developed based on widely used techniques of low-pass filter and Wiener filter, which can efficiently reduce noise without noticeable artifacts even in HR(S)TEM micrographs with contrast of variation of background and defects. The developed nonlinear filtering algorithm is particularly suitable for quantitative electron microscopy, and is also of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM. - Highlights: • A nonlinear filtering algorithm for denoising HR(S)TEM images is developed. • It can simultaneously handle both periodic and non-periodic features properly. • It is particularly suitable for quantitative electron microscopy. • It is of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM.
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.
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.
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.
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.
A Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets
Directory of Open Access Journals (Sweden)
C. Sharmeela
2006-01-01
Full Text Available This study presents a novel method to detect and classify power quality disturbances using wavelets. The proposed algorithm uses different wavelets each for a particular class of disturbance. The method uses wavelet filter banks in an effective way and does multiple filtering to detect the disturbances. A qualitative comparison of results shows the advantages and drawbacks of each wavelet when applied to the detection of the disturbances. This method is tested for a large class of test conditions simulated in MATLAB. Power quality monitoring together with the ability of the proposed algorithm to classify the disturbances will be a powerful tool for the power system engineers.
Noise reduction in LOS wind velocity of Doppler lidar using discrete wavelet analysis
Wu, Songhua; Liu, Zhishen; Sun, Dapeng
2003-12-01
The line of sight (LOS) wind velocity can be determined from the incoherent Doppler lidar backscattering signals. Noise and interference in the measurement greatly degrade the inversion accuracy. In this paper, we apply the discrete wavelet denoising method by using biorthogonal wavelets and adopt a distancedependent thresholds algorithm to improve the accuracy of wind velocity measurement by incoherent Doppler lidar. The noisy simulation data are processed and compared with the true LOS wind velocity. The results are compared by the evaluation of both the standard deviation and correlation coefficient.The results suggest that wavelet denoising with distance-dependent thresholds can considerably reduce the noise and interfering turbulence for wind lidar measurement.
Coherent noise removal in seismic data with dual-tree M-band wavelets
Duval, Laurent; Chaux, Caroline; Ker, Stéphan
2007-09-01
Seismic data and their complexity still challenge signal processing algorithms in several applications. The advent of wavelet transforms has allowed improvements in tackling denoising problems. We propose here coherent noise filtering in seismic data with the dual-tree M-band wavelet transform. They offer the possibility to decompose data locally with improved multiscale directions and frequency bands. Denoising is performed in a deterministic fashion in the directional subbands, depending of the coherent noise properties. Preliminary results show that they consistently better preserve seismic signal of interest embedded in highly energetic directional noises than discrete critically sampled and redundant separable wavelet transforms.
Redundant Wavelets on Graphs and High Dimensional Data Clouds
Ram, Idan; Cohen, Israel
2011-01-01
In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level linear operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform to image denoising, and show that it achieves denoising results that are close to those obtained with the BM3D algorithm.
IMPROVEMENT OF ANOMALY DETECTION ALGORITHMS IN HYPERSPECTRAL IMAGES USING DISCRETE WAVELET TRANSFORM
Kamal Jamshidi; Mohsen Zare Baghbidi; Ahmad Reza Naghsh Nilchi; Saeid Homayouni
2012-01-01
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four band...
Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform
Institute of Scientific and Technical Information of China (English)
YAN Yu-hua; WANG Hui-min; LI Shi-pu
2004-01-01
An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.
Application of K-SVD Wavelet Denoising to Gear Fault Diagnosis%K-SVD小波降噪在齿轮故障诊断中应用
Institute of Scientific and Technical Information of China (English)
钟也磐; 陈卫
2016-01-01
For the large noise disturbance in the early stage gear fault diagnosis, it is difficult to extract the fault features. In this paper, a new method of wavelet denoising method based on K-SVD sparse representation is proposed. This method can overcome the disadvantage of the conventional threshold method that it only deals with the wavelet coefficients one by one but ignores the whole structure of the coefficients. In this method, the wavelet coefficient structure characteristic is sufficiently considered. It has good robustness even in strong noise background. Through the analysis of simulated signals and measured signals of an aero-engine gear hub, the correctness and validity in engineering application of the proposed method are verified.%针对早期齿轮故障诊断中噪声干扰大，故障特征难以提取的问题提出基于K-SVD稀疏表示小波降噪算法。该算法克服传统小波阈值降噪算法只对小波系数进行逐点处理，而忽略小波系数整体架构的缺点，充分考虑小波系数结构特点，在强噪声下仍具有很好稳健性。通过对模拟信号和实测发动机减速器齿轮毂信号分析，证明小波降噪算法正确性和在实际工程应用中的价值。
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.
An improved image compression algorithm using binary space partition scheme and geometric wavelets.
Chopra, Garima; Pal, A K
2011-01-01
Geometric wavelet is a recent development in the field of multivariate nonlinear piecewise polynomials approximation. The present study improves the geometric wavelet (GW) image coding method by using the slope intercept representation of the straight line in the binary space partition scheme. The performance of the proposed algorithm is compared with the wavelet transform-based compression methods such as the embedded zerotree wavelet (EZW), the set partitioning in hierarchical trees (SPIHT) and the embedded block coding with optimized truncation (EBCOT), and other recently developed "sparse geometric representation" based compression algorithms. The proposed image compression algorithm outperforms the EZW, the Bandelets and the GW algorithm. The presented algorithm reports a gain of 0.22 dB over the GW method at the compression ratio of 64 for the Cameraman test image.
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.
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.
Chen, Hong-Yan; Zhao, Geng-Xing; Li, Xi-Can; Wang, Xiang-Feng; Li, Yu-Ling
2013-11-01
Taking the Qihe County in Shandong Province of East China as the study area, soil samples were collected from the field, and based on the hyperspectral reflectance measurement of the soil samples and the transformation with the first deviation, the spectra were denoised and compressed by discrete wavelet transform (DWT), the variables for the soil alkali hydrolysable nitrogen quantitative estimation models were selected by genetic algorithms (GA), and the estimation models for the soil alkali hydrolysable nitrogen content were built by using partial least squares (PLS) regression. The discrete wavelet transform and genetic algorithm in combining with partial least squares (DWT-GA-PLS) could not only compress the spectrum variables and reduce the model variables, but also improve the quantitative estimation accuracy of soil alkali hydrolysable nitrogen content. Based on the 1-2 levels low frequency coefficients of discrete wavelet transform, and under the condition of large scale decrement of spectrum variables, the calibration models could achieve the higher or the same prediction accuracy as the soil full spectra. The model based on the second level low frequency coefficients had the highest precision, with the model predicting R2 being 0.85, the RMSE being 8.11 mg x kg(-1), and RPD being 2.53, indicating the effectiveness of DWT-GA-PLS method in estimating soil alkali hydrolysable nitrogen content.
Wavelet Digital Watermarking with Unsupervised Learning
Institute of Scientific and Technical Information of China (English)
SHEKun; HUANGJuncai; ZHOUMingtian
2005-01-01
In this paper a novel technique for the digital watermarking of still color images based on the concept of color space transform, wavelet fusion and image denoising is presented. Our algorithm transforms R, G, B colorspace to R′, G′, B′ color space firstly, which constructs three grey images, then computes the 2nd-level discretewavelet decomposition of the three grey images. Embedding watermark fuses a grey image (watermark) with each of the three images' wavelet coefficients, then transforms R′, G′, B′ color space to R, G, B color space to obtain thewatermarked color image. When extracting watermark, at first two grey images X1, X2 from the two of three wavelet coefficients is distilled, then Independent component analyses (ICA) is used to denoise and get grey image (watermark) W. The results of our test show the superior performance of the technique and the great potential of the watermarking of photographic imagery.
Generalized Tree-Based Wavelet Transform
Ram, Idan; Cohen, Israel
2010-01-01
In this paper we propose a new wavelet transform applicable to functions defined on graphs, high dimensional data and networks. The proposed method generalizes the Haar-like transform proposed in \\cite{gavish2010mwot}, and it is similarly defined via a hierarchical tree, which is assumed to capture the geometry and structure of the input data. It is applied to the data using a multiscale filtering and decimation scheme, which can employ different wavelet filters. We propose a tree construction method which results in efficient representation of the input function in the transform domain. We show that the proposed transform is more efficient than both the 1D and 2D separable wavelet transforms in representing images. We also explore the application of the proposed transform to image denoising, and show that combined with a subimage averaging scheme, it achieves denoising results which are similar to the ones obtained with the K-SVD algorithm.
Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals
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Vijay S. Chourasia
2013-01-01
Full Text Available Fetal phonocardiography (fPCG based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering may result into loss of valuable diagnostic information from these signals. This calls for a robust, versatile, and adaptable denoising method applicable in different operative circumstances. In this work, a novel algorithm based on wavelet transform has been developed for denoising of fPCG signals. Successful implementation of wavelet theory in denoising is heavily dependent on selection of suitable wavelet basis function. This work introduces a new mother wavelet basis function for denoising of fPCG signals. The performance of newly developed wavelet is found to be better when compared with the existing wavelets. For this purpose, a two-channel filter bank, based on characteristics of fPCG signal, is designed. The resultant denoised fPCG signals retain the important diagnostic information contained in the original fPCG signal.
高重叠率下弹性成像应变估计值的小波去噪研究%Wavelet de-noising of strain estimates in elastography at high overlap
Institute of Scientific and Technical Information of China (English)
崔少国; 彭彩碧; 刘赟
2011-01-01
Object High overlap of data window is essential to improve axial resolution in elastogaphy.However, correlated errors in displacement estimates increase dramatically with the increase of the overlap, and generate the so-called "worm" artifacts. This paper presents a wavelet shrinkage de-noising in strain estimates to reduce the worm artifacts at high overlap. Methods Each of axial strain A-lines was decomposed using discrete wavelet transformation up to 3 levels. The high frequency components of every levels of wavelet coefficients were quantified by using soft threshold function according to different adaptive thresholds. Then the discrete wavelet reconstruction were performed to produce a wavelet shrinkage denoised strain line. Results The simulation results illustrated that the presented technique could efficiently denoise worm artifacts and enhance the elastogram performance indices such as elastographic SNRe and CNRe. Elastogram obtained by wavelet denoising had the closest correspondence with ideal strain image. In addition, the results also demonstrated that wavelet shrinkage de-noising applied in strain estimates could obtain better image quality parameters than that apphed in displacement estimates. The elastic phantom experiments also showed the similar elastogram performance improvement. Conclusion Wavelet shrinkage de-noising can efficiently denoise the worm artifacts noise of elastogram and improve the performance indices of elastogram while maintaining the high axial resolution.%目的 高的数据窗重叠率是提高弹性成像轴向分辨率的必要条件,但重叠率的增加会使位移估计的相关误差急剧增长,产生所谓的"蠕虫"噪声.本研究使用小波收缩法去除高重叠率下弹性图像蠕虫噪声.方法 对每一条轴向应变A-line先进行3级离散小波分解,然后根据4种自适应阈值之一使用软阈值函数对每一层小波高频系数进行量化,最后进行小波重构产生
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.
EEG signals processed by wavelet de-noising and blind source separation%基于小波消噪和盲源分离的脑电信号处理方法
Institute of Scientific and Technical Information of China (English)
罗志增; 徐斌
2011-01-01
针对脑电信号中噪声及夹杂的眼电、心电等伪迹,采用小波分解重构消噪和基于熵估计的RADICAL算法进行消除.对3路脑电观测信号进行小波消噪和白化处理后,通过雅可比旋转矩阵分别对其中的两两组合信号用RADICAL算法进行分离,得出最优分离矩阵完成盲源分离,并引入互相关系数、矩阵相关系数验证算法的有效性.实验结果表明:源信号未知的3路相互串扰脑电信号盲分离后各个分量之间的互相关系数近似为0,并且其矩阵相关系数每行均有大于0.95的值,优于常用的FastICA算法,说明该方法能有效去除脑电信号中噪声和伪迹.%In order to remove the noise and artifacts(VEOG (vertical ectro-oculogram), EKG (electrocardiogram)) in EEG signals, the wavelet decomposition and reconstruction denoising plus the RADICAL algorithm based on entropy estimates were adopted. After wavelet de-noing and pre-whit-ening, the optimal separation matrix was obtained by using RADICAL algorithm to process the EEG signals combined with Jacobi rotation matrix. Then, the paper introduced cross-correlation coefficient and matrix cross-correlation coefficient to prove the effectiveness of the proposed method. The experimental results indicate that the cross-correlation coefficient of each component for the three mutual crosstalk signals processed by RADICAL algorithm is approximate to 0 and each row of the matrix cross-correlation coefficient have the value larger than 0. 95 which is better than FastICA algorithm. It proves that the proposed method can effectively remove the noise and artifacts in EEG signals.
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.
Astronomical Image Denoising Using Dictionary Learning
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...
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.
Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System
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Zhang Yulin
2015-01-01
Full Text Available To address the limitation of conventional adaptive algorithm used for active noise control (ANC system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE, which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS and Decomposition and Reconstruction LMS algorithm (DR-LMS based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.
Directory of Open Access Journals (Sweden)
Wei Fan
2014-01-01
Full Text Available Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.
Adaptive Non-Linear Bayesian Filter for ECG Denoising
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Mitesh Kumar Sao
2014-06-01
Full Text Available The cycles of an electrocardiogram (ECG signal contain three components P-wave, QRS complex and the T-wave. Noise is present in cardiograph as signals being measured in which biological resources (muscle contraction, base line drift, motion noise and environmental resources (power line interference, electrode contact noise, instrumentation noise are normally pollute ECG signal detected at the electrode. Visu-Shrink thresholding and Bayesian thresholding are the two filters based technique on wavelet method which is denoising the PLI noisy ECG signal. So thresholding techniques are applied for the effectiveness of ECG interval and compared the results with the wavelet soft and hard thresholding methods. The outputs are evaluated by calculating the root mean square (RMS, signal to noise ratio (SNR, correlation coefficient (CC and power spectral density (PSD using MATLAB software. The clean ECG signal shows Bayesian thresholding technique is more powerful algorithm for denoising.
Institute of Scientific and Technical Information of China (English)
张茁生; 刘贵忠; 刘峰
2003-01-01
A new algorithm for reconstructing a signal from its wavelet transform modulus maxima is presented based on an iterative method for solutions to monotone operator equations in Hilbert spaces. The algorithm's convergence is proved. Numerical simulations for different types of signals are given. The results indicate that compared with Mallat's alternate projection method, the proposed algorithm is sim-pler, faster and more effective.
Multi-image gradient-based algorithms for motion measurement using wavelet transform
Institute of Scientific and Technical Information of China (English)
2008-01-01
A multi-image wavelet transform motion estimation algorithm based on gradient methods is presented by using the characteristic of wavelet transfom.In this algorithm,the accuracy can be improved greatly using data in many images to measure motions between two images.In combination with the reliability measure for constraints function,the reliable data constraints of the images were decomposed with multi-level simultaneous wavelet transform rather than the traditional coarse-to-fine approach.Compared with conventional methods,this motion measurement algorithm based on multi-level simultaneous wavelet transform avoids propagating errors between the decomposed levels.Experimental simulations show that the implementation of this algo rithm is simple,and the measurement accuracy is improved.
Institute of Scientific and Technical Information of China (English)
何建军; 任震; 黄雯莹; 周宏; 林涛
1999-01-01
With a complex wavelet function, a new real-time recursive algorithm of wavelet transform (WT) is analyzed in detail. Compared with the existing recursive algorithm in two directions, the computing time is greatly redueed in response to faults signals in power systems, and the same recursive algorithm can be generalized to other wavelet functions. With the phases and magnitudes of complex WT coefficients under the fast recursive algorithm, a method to detect faults signals of power systems is presented. Lastly, the analyzing results of some signals show that it is effective and practical for the complex wavelet and its real-time recursive algorithm to detect faults of power systems.
On optimisation of wavelet algorithms for non-perfect wavelet compression of digital medical images
Ricke, J
2001-01-01
Aim: Optimisation of medical image compression. Evaluation of wavelet-filters for wavelet-compression. Results: Application of filters with different complexity results in significant variations in the quality of image reconstruction after compression specifically in low frequency information. Filters of high complexity proved to be advantageous despite of heterogenous results during visual analysis. For high frequency details, complexity of filters did not prove to be of significant impact on image after reconstruction.
An Improved Watermarking Algorithm to Colour Image Based on Wavelet Domain
Directory of Open Access Journals (Sweden)
Yinglan Fang
2013-07-01
Full Text Available This paper has brought forward an improved non-blind watermarking algorithm based on discrete wavelet transform. Watermarking applies special meaningful color image. Before embedded watermark, the algorithm requires needs the watermarking image and carrier image to separate color and transform discrete wavelet. Then the watermark’s low frequency sub-graph and high low sub-graph are respectively embedded into carrier image using additive watermark embedding rules and iterative mixed method. The experiment results have showed that the algorithm has good concealment and improve the robustness of the algorithm.
Institute of Scientific and Technical Information of China (English)
曹雪; 余立功; 杨静宇
2011-01-01
The recognition of frontal facial appearance with illumination is a difficult task for face recognition. In this paper, a novel illumination invariant extraction method was proposed to deal with the illumination problem based on wavelet transform and denoising model. The illumination invariant was extracted in wavelet domain by using wavelet-based denoising techniques. Through manipulating the high frequency wavelet coefficient combined with denoising model, the edge features of the illumination invariants were enhanced and more useful information was restored in illumination invariants, which could lead to an excellent face recognition performance. The experimental results on Yale face database B and CMU PIE face database show that satisfactory recognition rate can be achieved by the proposed method.%针对正面光照人脸识别的难点,提出了一种应用小波变换和去噪模型的光照不变人脸识别算法.利用对图像的高频小波系数进行处理并运用去噪模型,提取光照人脸图像中的光照不变量,同时增强图像边缘特征,这有利于提取的光照不变量保持更多的人脸识别信息.在Yale B和CMU PIE人脸库上的实验结果表明,所提算法可以显著提高光照人脸图像的识别率.
The Brera Multi-scale Wavelet (BMW) ROSAT HRI source catalog; 1, the algorithm
Lazzati, D; Rosati, P; Panzera, M R; Tagliaferri, G; Lazzati, Davide; Campana, Sergio; Rosati, Piero; Panzera, Maria Rosa; Tagliaferri, Gianpiero
1999-01-01
We present a new detection algorithm based on the wavelet transform for the analysis of high energy astronomical images. The wavelet transform, due to its multi-scale structure, is suited for the optimal detection of point-like as well as extended sources, regardless of any loss of resolution with the off-axis angle. Sources are detected as significant enhancements in the wavelet space, after the subtraction of the non-flat components of the background. Detection thresholds are computed through Monte Carlo simulations in order to establish the expected number of spurious sources per field. The source characterization is performed through a multi-source fitting in the wavelet space. The procedure is designed to correctly deal with very crowded fields, allowing for the simultaneous characterization of nearby sources. To obtain a fast and reliable estimate of the source parameters and related errors, we apply a novel decimation technique which, taking into account the correlation properties of the wavelet transf...
Improvement of Anomoly Detection Algorithms in Hyperspectral Images using Discrete Wavelet Transform
Baghbidi, Mohsen Zare; Nilchi, Ahmad Reza Naghsh; Homayouni, Saeid; 10.5121/sipij.2011.2402
2012-01-01
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of "Wavelet transform" matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT's power in extracting approximation coefficients of signal, which contain the main behaviour of sig...
An improved PSO algorithm and its application in seismic wavelet extraction
Directory of Open Access Journals (Sweden)
Yongshou Dai
2011-08-01
Full Text Available The seismic wavelet estimation is finally a multi-dimension, multi-extreme and multi-parameter optimization problem. PSO is easy to fall into local optimum, which has simple concepts and fast convergence. This paper proposes an improved PSO with adaptive parameters and boundary constraints, in ensuring accuracy of the algorithm optimization and fast convergence. Simulation results show that the methods have good applicability and stability for seismic wavelet extraction.
Directory of Open Access Journals (Sweden)
Noureddine Aloui
2015-02-01
Full Text Available This paper presents an optimized speech compression algorithm using discrete wavelet transform, and its real time implementation on fixed-point digital signal processor (DSP. The optimized speech compression algorithm presents the advantages to ensure low complexity, low bit rate and achieve high speech coding efficiency, and this by adding a voice activity detector (VAD module before the application of the discrete wavelet transform. The VAD module avoids the computation of the discrete wavelet coefficients during the inactive voice signal. In addition, a real-time implementation of the optimized speech compression algorithm is performed using fixed-point processor. The optimized and the original algorithms are evaluated and compared in terms of CPU time (sec, Cycle count (MCPS, Memory consumption (Ko, Compression Ratio (CR, Signal to Noise Ratio (SNR, Peak Signal to Noise Ratio (PSNR and Normalized Root Mean Square Error (NRMSE.
Super-resolution image restoration algorithm based on orthogonal discrete wavelet transform
Institute of Scientific and Technical Information of China (English)
Yangyang Liu(刘扬阳); Weiqi Jin(金伟其); Binghua Su(苏秉华)
2004-01-01
By using orthogonal discrete wavelet transform(ODWT)and generalized cross validation(GCV),and combining with Luck-Richardson algorithm based on Poisson-Markovmodel (MPML),several new superresolution image restoration algorithms are proposed.According to simulation experiments for practical images,all the proposed algor ithms could retain image details better than MPML,and be more suitable to low signal-to-noise ratio(SNR)images.The single operation wavelet MPML(SW-MPML)algorithm and MPML algorithm based on single operation wavelet transform(MPML-SW)avoid the iterative operation of self-adaptive parameter in MPML particularly,and improve operating speed and precision.They are instantaneous to super-resolution image restoration process and have extensive application foreground.
An Improved Singularity Computing Algorithm Based on Wavelet Transform Modulus Maxima Method
Institute of Scientific and Technical Information of China (English)
ZHAO Jian; XIE Duan; FAN Xun-li
2006-01-01
In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.
Energy Technology Data Exchange (ETDEWEB)
Ochoa Domínguez, Humberto de Jesús, E-mail: hochoa@uacj.mx [Departamento de Ingeniería Eléctrica y computación, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chih. (Mexico); Máynez, Leticia O. [Departamento de Ingeniería Eléctrica y computación, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chih. (Mexico); Vergara Villegas, Osslan O. [Departamento de Ingeniería Industrial, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chih. (Mexico); Mederos, Boris; Mejía, José M.; Cruz Sánchez, Vianey G. [Departamento de Ingeniería Eléctrica y computación, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chih. (Mexico)
2015-06-01
PET allows functional imaging of the living tissue. However, one of the most serious technical problems affecting the reconstructed data is the noise, particularly in images of small animals. In this paper, a method for high-resolution small animal 3D PET data is proposed with the aim to reduce the noise and preserve details. The method is based on the estimation of the non-subsampled Haar wavelet coefficients by using a linear estimator. The procedure is applied to the volumetric images, reconstructed without correction factors (plane reconstruction). Results show that the method preserves the structures and drastically reduces the noise that contaminates the image.
Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager's Energy Operator
Institute of Scientific and Technical Information of China (English)
DU Boxue; OUYANG Mingjian; WU Yuan; WEI Guozhong
2005-01-01
To develop a measurement system for monitoring partial discharge (PD) without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager's energy operators was presented.Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences.Acoustic signals were collected and decomposed into 10 levels by wavelet transform into approximation and detail components."Daubechies 25"was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals.Compared with conventional wavelet denoising method,Teager's energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was pulse amplitude.
A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization
Institute of Scientific and Technical Information of China (English)
YANG YongMing; XU Chao
2008-01-01
As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.
ECG De-noising: A comparison between EEMD-BLMS and DWT-NN algorithms.
Kærgaard, Kevin; Jensen, Søren Hjøllund; Puthusserypady, Sadasivan
2015-08-01
Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of the heart and thereby to detect the abnormalities. However, these signals are often obscured by artifacts from various sources and minimization of these artifacts are of paramount important. This paper proposes two adaptive techniques, namely the EEMD-BLMS (Ensemble Empirical Mode Decomposition in conjunction with the Block Least Mean Square algorithm) and DWT-NN (Discrete Wavelet Transform followed by Neural Network) methods in minimizing the artifacts from recorded ECG signals, and compares their performance. These methods were first compared on two types of simulated noise corrupted ECG signals: Type-I (desired ECG+noise frequencies outside the ECG frequency band) and Type-II (ECG+noise frequencies both inside and outside the ECG frequency band). Subsequently, they were tested on real ECG recordings. Results clearly show that both the methods works equally well when used on Type-I signals. However, on Type-II signals the DWT-NN performed better. In the case of real ECG data, though both methods performed similar, the DWT-NN method was a slightly better in terms of minimizing the high frequency artifacts.
Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.
Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi
2017-05-28
In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.
Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis
Qin, Yi; Xing, Jianfeng; Mao, Yongfang
2016-08-01
Aimed at solving the key problem in weak transient detection, the present study proposes a new transient feature extraction approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding. Firstly, a fast optimization algorithm based on the Shannon entropy is developed to obtain the optimized Morlet wavelet parameter. Compared to the existing Morlet wavelet parameter optimization algorithm, this algorithm has lower computation complexity. After performing the optimized Morlet wavelet transform on the analyzed signal, the kurtosis index is used to select the characteristic scales and obtain the corresponding wavelet coefficients. From the time-frequency distribution of the periodic impulsive signal, it is found that the transient signal can be reconstructed by the wavelet coefficients at several characteristic scales, rather than the wavelet coefficients at just one characteristic scale, so as to improve the accuracy of transient detection. Due to the noise influence on the characteristic wavelet coefficients, the adaptive soft-thresholding method is applied to denoise these coefficients. With the denoised wavelet coefficients, the transient signal can be reconstructed. The proposed method was applied to the analysis of two simulated signals, and the diagnosis of a rolling bearing fault and a gearbox fault. The superiority of the method over the fast kurtogram method was verified by the results of simulation analysis and real experiments. It is concluded that the proposed method is extremely suitable for extracting the periodic impulsive feature from strong background noise.
Institute of Scientific and Technical Information of China (English)
高伟; 祖悦; 王伟; 兰海钰; 张亚
2012-01-01
In the application of SINS using fiber optic gyroscope (FOG) as the main inertial measurement sensor, the real-time de-noising of the FOG non-deterministic random drift significantly improves the SINS initial alignment and navigation accuracy. Considering the restriction of traditional wavelet de-noising performance and the real-time problem, an improved method for the real-time de-noising of the FOG output signal using the second generation wavelet transform combined with hard threshold, mandatory noise reduction and sliding data window is proposed. Sinusoidal signal and FOG output signal experiments were carried out using MATLAB. Compared with traditional wavelet real-time de-noising performance, the experiment results show that the proposed method can both enhance the de-noising performance and reduce the SINS output attitude error under the premise of great theoretical computing speed increasing.%在以光纤陀螺为主要惯性敏感元件的捷联惯导系统中,陀螺输出信号中的非确定性随机漂移的实时滤除,对提高实际系统的初始对准精度及导航精度均具有重要的意义.考虑到传统小波阈值法的去噪性能及实时性问题,提出了一种基于第二代小波变换,并结合硬阈值、强制降噪和带滑动数据窗的光纤陀螺信号实时降噪改进方案.利用MATLAB进行了正弦信号和光纤陀螺输出信号的模拟实时降噪实验,并与一代小波实时降噪方案进行了比较,验证了改进方案在理论计算速度大幅提升的前提下,降噪性能得到提高,进而减小了系统输出的姿态误差.
Institute of Scientific and Technical Information of China (English)
黄建招; 谢建; 高钦和; 邓刚峰
2012-01-01
An improved self-adaptive de-noising method based on discrete stationary wavelet transform was given. The problem of the singular-points oscillation effects,was solved by the redundant nature of the discrete stationary wavelet transform. Aiming to the shortage of ignoring the noise influence in approximation coefficients, both detail and approximation coefficients were self-adaptive denoised using the thresholds,which were estimated by the noise intensity accordingly. At last,the method was applied to the typical signals de-noisong experiments in different SNR. The simulation result shows that in qualitative and quantitative indicators, the de-noising effect of the method is better than the traditional discrete binary wavelet transform.%提出一种基于离散平稳小波的改进自适应降噪方法.首先,利用离散平稳小波的冗余特性,解决离散二进小波变换降噪方法在奇异点存在振荡效应的问题;其次,针对传统小波阈值降噪算法忽略尺度系数噪声影响的不足,利用噪声强度估计各分解层阈值,对尺度和小波系数同时进行自适应降噪;最后,将此方法应用于不同信噪比下典型信号的降噪对比试验.仿真结果表明:该方法在消噪的定性和定量指标上,整体优于传统离散二进小波方法,消噪效果改善明显.
Institute of Scientific and Technical Information of China (English)
殷勇; 周秋香; 于慧春; 肖涛
2011-01-01
For the white noise always embeds in signals of gas sensor array, there must be bad influence on the accuracy and reliability of test results while micro or trace organophosphorus pesticide residues in vegetables detection. To solve the problem, acephate and phoxim were selected as test object. A denoising method of gas sensor array signals based on wavelet packet decomposition and reconstruction was practiced. With the help of principal component analysis ( PCA) and Fisher discriminant analysis ( FDA) , the identification results of different concentrations corresponding to the two kinds of pesticide residues were explored and compared, respectively. The results showed that the different concentrations samples corresponding to acephate and phoxim were all well discriminated after the gas sensor array signals treated by the wavelet packet denoising. Therefore, the wavelet packet denoising method could improve identification effect of pesticide residues in vegetables using gas sensor array.%针对有机磷农药气敏传感阵列测试信号含有噪声,严重影响测试结果准确性与可靠性这一问题,选择辛硫磷和乙酰甲胺磷农药残留为研究对象,采用基于小波包分解与重构的气体传感阵列信号降噪方法,并借助主成分分析(PCA)和Fisher判别分析(FDA),分别研究了信号降噪前后两种农药不同质量比的鉴别情况.结果表明:传感阵列信号降噪后两种农药的不同残留样品均能被鉴别区分.小波包降噪可有效地提高气敏传感阵列对蔬菜农药残留的鉴别效果.
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.
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.
Institute of Scientific and Technical Information of China (English)
Zhou Xiaohui; Wang Gang; Wang Baoqin
2011-01-01
The purpose of this paper is to construct an orthogonal Armlet multi-wavelets with multiplicity r and dilation factor a.Firstly,the definition of Armlets with dilation factor a is proposed in this paper.Based on the Two-scale Similar Transform (TST),the notion of the Para-unitary A-scale Similar Transform (PAST) is introduced,and we also give the transform on the all two-scale matrix symbols of the multi-wavelets with dilation a.Then we show that the PAST and the transform on the matrix symbols of the multi-wavelets keep the orthogonality of the multi-wavelets system.We discuss the condition that a- 1 multi-wavelets corresponding to the multi-scaling functions are all Armlets.After performing the PAST and the transform on the matrix symbols of the multi-wavelets,the multi-scaling function can be balanced and the corresponding multi-wavelets can be Armlets at the same time.The construction of Armlets with high order is also discussed.At last,by a given example,we can conclude that the algorithm is feasible and efficient.
IMPROVEMENT OF ANOMALY DETECTION ALGORITHMS IN HYPERSPECTRAL IMAGES USING DISCRETE WAVELET TRANSFORM
Directory of Open Access Journals (Sweden)
Kamal Jamshidi
2012-01-01
Full Text Available Recently anomaly detection (AD has become an important application for target detection in hyperspectralremotely sensed images. In many applications, in addition to high accuracy of detection we need a fast andreliable algorithm as well. This paper presents a novel method to improve the performance of current ADalgorithms. The proposed method first calculates Discrete Wavelet Transform (DWT of every pixel vectorof image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform”matrix which are the approximation of main image. In this research some benchmark AD algorithmsincluding Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared ImagingSpectrometer (AVIRIS hyperspectral datasets. Experimental results demonstrate significant improvementof runtime in proposed method. In addition, this method improves the accuracy of AD algorithms becauseof DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour ofsignal, and abandon the redundant information in hyperspectral image data.
基于小波降噪的偏振耦合检测分析%Analysis of polarization mode coupling of PMF based on wavelet denoising method
Institute of Scientific and Technical Information of China (English)
郭振武; 陈信伟; 张红霞; 贾大功; 刘铁根
2011-01-01
理论分析了基于白光干涉的保偏光纤偏振模式耦合检测原理,并以迈克尔逊干涉仪对保偏光纤的偏振耦合的耦合强度和耦合点的位置进行了测试.由于机械式扫描干涉仪和信号处理电路会受到环境干扰和各种噪声的影响,限制了系统偏振耦合检测的灵敏度,应用小波变换对检测信号进行了分析,采用软阈值方法降噪处理,有效地去除了各种干扰和噪声,提高了干涉信号的信噪比3.2dB,耦合强度测试灵敏度也相应地得到了提高,增强了测试系统检测微弱偏振耦合的能力.%The principle of the cross coupling in polarization maintaining fiber based on white-light interferometry is presented The coupling intensity and coupling position are measured by using Michelson-interferometer. The sensitivity of detection system is limited by environmental disturbance and mixture noise in the course of scanning measurement of the mechanical interferometer and signal processing. A wavelet soft threshold denoising method is adopted to process the interference signals. And then the noises are strongly restrained and the signal-to-noise ratio of the output signal is improved about 3. 2 dB. The sensitivity increases correspondingly, and the ability of detecting weak coupling is improved.
Zhang, Qiushi; Yang, Xueqian; Yao, Li; Zhao, Xiaojie
2017-03-27
Working memory (WM) refers to the holding and manipulation of information during cognitive tasks. Its underlying neural mechanisms have been explored through both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Trial-by-trial coupling of simultaneously collected EEG and fMRI signals has become an important and promising approach to study the spatio-temporal dynamics of such cognitive processes. Previous studies have demonstrated a modulation effect of the WM load on both the BOLD response in certain brain areas and the amplitude of P3. However, much remains to be explored regarding the WM load-dependent relationship between the amplitude of ERP components and cortical activities, and the low signal-to-noise ratio (SNR) of the EEG signal still poses a challenge to performing single-trial analyses. In this paper, we investigated the spatio-temporal activities of P3 during an n-back verbal WM task by introducing an adaptive wavelet denoiser into the extraction of single-trial P3 features and using general linear model (GLM) to integrate simultaneously collected EEG and fMRI data. Our results replicated the modulation effect of the WM load on the P3 amplitude. Additionally, the activation of single-trial P3 amplitudes was detected in multiple brain regions, including the insula, the cuneus, the lingual gyrus (LG), and the middle occipital gyrus (MOG). Moreover, we found significant correlations between P3 features and behavioral performance. These findings suggest that the single-trial integration of simultaneous EEG and fMRI signals may provide new insights into classical cognitive functions.
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
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.
Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm
Institute of Scientific and Technical Information of China (English)
JIANG Minghu(江铭虎); DENG Beixing(邓北星); Georges Gielen
2004-01-01
Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.
Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin
2017-04-01
An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.
Compression of multispectral Landsat imagery using the Embedded Zerotree Wavelet (EZW) algorithm
Shapiro, Jerome M.; Martucci, Stephen A.; Czigler, Martin
1994-01-01
The Embedded Zerotree Wavelet (EZW) algorithm has proven to be an extremely efficient and flexible compression algorithm for low bit rate image coding. The embedding algorithm attempts to order the bits in the bit stream in numerical importance and thus a given code contains all lower rate encodings of the same algorithm. Therefore, precise bit rate control is achievable and a target rate or distortion metric can be met exactly. Furthermore, the technique is fully image adaptive. An algorithm for multispectral image compression which combines the spectral redundancy removal properties of the image-dependent Karhunen-Loeve Transform (KLT) with the efficiency, controllability, and adaptivity of the embedded zerotree wavelet algorithm is presented. Results are shown which illustrate the advantage of jointly encoding spectral components using the KLT and EZW.
Lossless Image Compression Using A Simplified MED Algorithm with Integer Wavelet Transform
Directory of Open Access Journals (Sweden)
Mohamed M. Fouad
2013-11-01
Full Text Available In this paper, we propose a lossless (LS image compression technique combining a prediction step with the integer wavelet transform. The prediction step proposed in this technique is a simplified version of the median edge detector algorithm used with JPEG-LS. First, the image is transformed using the prediction step and a difference image is obtained. The difference image goes through an integer wavelet transform and the transform coefficients are used in the lossless codeword assignment. The algorithm is simple and test results show that it yields higher compression ratios than competing techniques. Computational cost is also kept close to competing techniques.
Optical phase extraction algorithm based on the continuous wavelet and the Hilbert transforms
Bahich, Mustapha; Barj, Elmostafa
2010-01-01
In this paper we present an algorithm for optical phase evaluation based on the wavelet transform technique. The main advantage of this method is that it requires only one fringe pattern. This algorithm is based on the use of a second {\\pi}/2 phase shifted fringe pattern where it is calculated via the Hilbert transform. To test its validity, the algorithm was used to demodulate a simulated fringe pattern giving the phase distribution with a good accuracy.
An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms.
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.
De-noising of Vertical Wheel-rail Contact Force Signals Using Wavelet Transform%基于小波变换的轮轨垂向力信号降噪
Institute of Scientific and Technical Information of China (English)
黄辉; 雷晓燕; 刘庆杰
2016-01-01
轮轨力应变信号在采集过程中，由于噪声干扰的存在，将严重影响所采集数据的准确性。针对轮轨力应变信号中存在的基线漂移和随机白噪声，提出基于小波变换的去噪方法：采用db 6小波基，根据小波多分辨率分析理论，以大尺度分解的逼近分量估计基线漂移成分，从而消除基线漂移；对于随机白噪声则是运用小波阈值去噪法，先根据离散有限序列的自相关函数确定小波分解的最优分解层数，然后采用最小最大阈值以及硬阈值函数，从而实现对白噪声的滤除。仿真与实测数据分析都表明该去噪法能达到比较理想的效果。%The accuracy of the wheel-rail strain signals can be seriously ruined by the disturbance of noise. In this paper, a denoising method based on wavelet transform was proposed for elimination of baseline drift and random white noise. The baseline drift was eliminated by using db6 wavelet bases and the estimation of high-1evel approximation based on wavelet multi-resolution analysis. While the random white noise was eliminated by applying wavelet threshold denoising method. First of all, the optimal decomposition level of the wavelet transformation was determined by applying the self-correlation function of discrete finite sequence. Then, the minimum and maximum thresholds and hard shrinking function were adopted to filter the white noise. The analysis of simulation and the measured data show that this denoising method can achieve ideal effect.
Denoising of Pulse Wave Signal Based on Wavelet Analysis%基于小波分析的脉搏波信号去噪
Institute of Scientific and Technical Information of China (English)
徐洁; 付强
2012-01-01
In order to remove the noise signals such as the EMG interference and baseline drift from pulse-wave signals, an improved spatial correlation filtering algorithm was presented to target the poor performances of traditional filtering methods. After the signals were decomposed with wavelet, we added an adaptive adjustment factor to the wavelet transform values. At last, the signals were reconstructed with retained wavelet coefficients. This new algorithm keeps the main edges of signals well while getting rid of noises mostly. Simulation results indicate that the proposed de-oising method avoids pseudo-Gibbs phenomena and gives better SNR gains and RMSE performance.%脉搏波信号采集过程中存在引入的肌电干扰和基线漂移等噪声,引起脉搏信号不准确.针对传统滤波方法滤除脉搏波噪声性能较低的特点,提出了一种改进的空域相关滤波方法.将信号小波分解后,在小波变换值的相关量中加入一个自适应调整系数,最后用保留的小波系数对原信号进行重构；改进方法具有良好的自适应性能和显著的滤波效果,在有效去除噪声的同时,很好地保留了信号的主要细节.仿真结果表明,改进算法去噪效果好,同时减小误差,提高了信噪比,为脉搏分析提供了准确信息.
Image coding using parallel implementations of the embedded zerotree wavelet algorithm
Creusere, Charles D.
1996-03-01
We explore here the implementation of Shapiro's embedded zerotree wavelet (EZW) image coding algorithms on an array of parallel processors. To this end, we first consider the problem of parallelizing the basic wavelet transform, discussing past work in this area and the compatibility of that work with the zerotree coding process. From this discussion, we present a parallel partitioning of the transform which is computationally efficient and which allows the wavelet coefficients to be coded with little or no additional inter-processor communication. The key to achieving low data dependence between the processors is to ensure that each processor contains only entire zerotrees of wavelet coefficients after the decomposition is complete. We next quantify the rate-distortion tradeoffs associated with different levels of parallelization for a few variations of the basic coding algorithm. Studying these results, we conclude that the quality of the coder decreases as the number of parallel processors used to implement it increases. Noting that the performance of the parallel algorithm might be unacceptably poor for large processor arrays, we also develop an alternate algorithm which always achieves the same rate-distortion performance as the original sequential EZW algorithm at the cost of higher complexity and reduced scalability.
A COMPRESSION ALGORITHM FOR ECG BASED ON INTEGER LIFTING SCHEME WAVELET TRANSFORM
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In view of the shortcomes of conventional ElectroCardioGram (ECG) compression algorithms, such as high complexity of operation and distortion of reconstructed signal, a new ECG compression encoding algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) is brought out after studying the integer lifting scheme wavelet transform in detail. The proposed algorithm modifies zero-tree structure of SPIHT, establishes single dimensional wavelet coefficient tree of ECG signals and enhances the efficiency of SPIHT-encoding by distributing bits rationally, improving zero-tree set and ameliorating classifying method. For this improved algorithm, floating-point computation and storage are left out of consideration and it is easy to be implemented by hardware and software. Experimental results prove that the new algorithm has admirable features of low complexity,high speed and good performance in signal reconstruction. High compression ratio is obtained with high signal fidelity as well.
Institute of Scientific and Technical Information of China (English)
YIN Hong; CHEN Zeng-qiang; YUAN Zhu-zhi
2006-01-01
@@ A hyperchaos-based watermarking algorithm is developed in the wavelet domain for images.The algorithm is based on discrete wavelet transform and combines the communication model with side information.We utilize a suitable scale factor to scale host image,then construct cosets for embedding digital watermarking according to scale version of the host image.Our scheme makes a tradeoff between imperceptibility and robustness,and achieves security.The extraction algorithm is a blind detection algorithm which retrieves the watermark without the original host image.In addition,we propose a new method for watermark encryption with hyperchaotic sequence.This method overcomes the drawback of small key space of chaotic sequence and improves the watermark security.Simulation results indicate that the algorithm is a well-balanced watermarking method that offers good robustness and imperceptibility.
Distributed edge detection algorithm based on wavelet transform for wireless video sensor network
Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng
2011-05-01
Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.
Using Invariant Translation to Denoise Electroencephalogram Signals
Directory of Open Access Journals (Sweden)
Janett Walters-Williams
2011-01-01
Full Text Available Problem statement: Because of the distance between the skull and the brain and their different resistivitys, Electroencephalogram (EEG recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdiagnosis, as such it is necessary to remove them before further analysis and processing can be done. Denoising is often done with Independent Component Analysis algorithms but of late Wavelet Transform has been utilized. Approach: In this study we utilized one of the newer Wavelet Transform methods, Translation-Invariant, to deny EEG signals. Different EEG signals were used to verify the method using the MATLAB software. Results were then compared with those of renowned ICA algorithms Fast ICA and Radical and evaluated using the performance measures Mean Square Error (MSE, Percentage Root Mean Square Difference (PRD and Signal to Noise Ratio (SNR. Results: Experiments revealed that Translation-Invariant Wavelet Transform had the smallest MSE and PRD while having the largest SNR. Conclusion/Recommendations: This indicated that it performed superior to the ICA algorithms producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain.
Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain
Liu, Yipeng; Jin, Jing; Wang, Qiang; Shen, Yi
2012-10-01
Speckle reduction is a difficult task for ultrasound image processing because of low resolution and contrast. As a novel tool of image analysis, quaternion wavelet (QW) has some superior properties compared to discrete wavelets, such as nearly shift-invariant wavelet coefficients and phase-based texture presentation. We aim to exploit the excellent performance of speckle reduction in quaternion wavelet domain based on the soft thresholding method. First, we exploit the characteristics of magnitude and phases in quaternion wavelet transform (QWT) to the denoising application, and find that the QWT phases of the images are little influenced by the noises. Then we model the QWT magnitude using the Rayleigh distribution, and derive the thresholding criterion. Furthermore, we conduct several experiments on synthetic speckle images and real ultrasound images. The performance of the proposed speckle reduction algorithm, using QWT with soft thresholding, demonstrates superiority to those using discrete wavelet transform and classical algorithms.
A wavelet watermarking algorithm based on a tree structure
Guitart Pla, Oriol; Lin, Eugene T.; Delp, Edward J., III
2004-06-01
We describe a blind watermarking technique for digital images. Our technique constructs an image-dependent watermark in the discrete wavelet transform (DWT) domain and inserts the watermark in the most signifcant coefficients of the image. The watermarked coefficients are determined by using the hierarchical tree structure induced by the DWT, similar in concept to embedded zerotree wavelet (EZW) compression. If the watermarked image is attacked or manipulated such that the set of significant coefficients is changed, the tree structure allows the correlation-based watermark detector to recover synchronization. Our technique also uses a visual adaptive scheme to insert the watermark to minimize watermark perceptibility. The visual adaptive scheme also takes advantage of the tree structure. Finally, a template is inserted into the watermark to provide robustness against geometric attacks. The template detection uses the cross-ratio of four collinear points.
小波分析中4种去噪方法的分析比较%The comparison of four kinds of methods of denoising based on wavelet analysis
Institute of Scientific and Technical Information of China (English)
李战明; 张晓东
2015-01-01
Four different kinds of denoising methods based on wavelet analysis were analysed in this paper, and in order to look for the most suitable methods among them to remove the EMG interference from ECG signals in the practical application. These methods adopted the soft, the hard and the improved threshold functions respectively. Use MATLAB to do experiments with the ECG signals provided by the MIT-BIH database, and select the most suitable methods according to the comparison results of the de-noising effects and the time needed. The discrete wavelet transform threshold method gets a poor denois-ing effect when using the three kinds of threshold functions, the translation invariant wavelet threshold method needs much computation time itself, and the stationary wavelet transform threshold method and the lifting wavelet transform threshold method get a good denoising effect and need a little time relatively when using the improved threshold function. The stationary wavelet transform threshold method combined with the improved threshold function and the lifting wavelet transform threshold method combined with the improved threshold function are the most suitable methods to remove the EMG interference among the four kinds of methods.%分析了基于小波分析的4种不同的去噪方法，并在其中寻找最适宜在实际中应用的心电信号中肌电干扰的去除方法。4种去噪方法分别采用软、硬及改进等3种阈值函数，通过MATLAB对MIT-BIH数据库中所提供的心电信号进行实验分析，根据去噪效果及所需时间对比结果判断最适宜的去噪方法。离散小波变换阈值法在采用3种阈值函数时去噪效果均较差，平移不变量小波阈值法本身运算量过大，平稳小波变换阈值法与提升小波变换阈值法在采用改进阈值函数时去噪效果好且所需时间相对较少。采用改进阈值函数的平稳小波变换阈值法与采用改进阈值函数的提
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.
De-noising of high-speed turnout vibration signals based on wavelet threshold%基于小波阈值的高速道岔振动信号降噪
Institute of Scientific and Technical Information of China (English)
周祥鑫; 王小敏; 杨扬; 郭进; 王平
2014-01-01
Turnout vibration signals are an important information in high-speed turnout damage monitoring.As the signals are interfered by strong noise during the process of field acquisition and transmission,the accuracy of turnout damage identification based on its noisy vibration signals is declined seriously.To solve this problem,a denoising method is generally employed before turnout damage identification.The complex and noisy vibration samples from a site, however,raise the hurdle of denoising.Here,an effective denoising method based on wavelet threshold for turnout vibration signals was proposed.The selection of parameters,such as,wavelet basis,decomposition scale,threshold criteria and threshold function was empirically discussed for wavelet threshold denoising. Then, turnout damage identification analysis was conducted with the principal component analysis (PCA)of frequency response functions (FRF) and average Mahalanobis distance (MD).The experimental results showed that the proposed method can be used to reduce the noise interference effectively for turnout damage identification,and create better conditions for further damage analysis.%在高速道岔伤损监测中，道岔振动信号是道岔伤损监测的重要信息来源，鉴于该信号在采集和传输过程中噪声干扰严重，影响伤损识别的准确性，研究了一种基于小波阈值的高速道岔振动信号降噪方法。详细讨论了道岔振动信号降噪过程中小波基、分解层数、阈值准则、阈值函数等参数的选择，并利用频响函数、主元分析和平均马氏距离分析降噪处理对伤损识别的影响。实验结果表明，该方法能有效降低噪声对伤损识别的干扰，为进一步对道岔进行伤损分析创造了良好的条件。
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.
Sonar target enhancement by shrinkage of incoherent wavelet coefficients.
Hunter, Alan J; van Vossen, Robbert
2014-01-01
Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.
Institute of Scientific and Technical Information of China (English)
陈勇; 孙虎儿; 王志武; 苏飞
2013-01-01
笔者提出了一种基于提升小波降噪与局域均值分解(Local Mean Decom position,LMD)的转子故障特征提取方法.LMD在分析非线性、非平稳信号方面效果较好,但是对噪声较敏感.为了消除噪声对LMD分解效果的影响,先用提升小波对原始信号降噪,然后对去噪信号进行LMD分解,选取有用的PF分量进行频谱分析,并提取出转子故障特征.通过仿真试验和转子故障特征提取试验,证明了该方法在提取转子故障特征中的有效性能.%A method of extracting rotor fault features based on lifting wavelet denoising and local mean decomposition (LMD) was put forward.The LMD was good at analyzing the nonlinear and nonstationary signals,while sensitive to noises.In order to eliminate influence of noises on LMD decomposition effects,lifting wavelet was applied to denoise original signals,and then the denoised signals was decomposed by LMD.After that,effective PF components were selected to conduct spectral analysis,so as to extract the rotor fault features.The contrast between simulation with actual rotor fault feature extracting test showed the proposed method was effective in extraction of rotor fault features.
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.
Removal of correlated noise by modeling the signal of interest in the wavelet domain.
Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried
2009-06-01
Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the "signal of interest" and that is applicable to correlated noise. We combine the intrascale model with a hidden Markov tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Institute of Scientific and Technical Information of China (English)
张雯雯; 刘黎平
2009-01-01
The method of adaptive denoising based on discrete wavelet transform (DWT) provides a feasible solution for radar signal filtering, but DWT does not have the characteristics of translation invariance of the wavelet coefficients. If signal reconstruction is not done with the same wavelet, it will cause considerable reconstruction errors.To deal with this phenomenon, this paper proposes an adaptive denoising method based on lifting static wavelet packet transformation. The authors developed the lifting method for static wavelet packets, and designed corresponding steps to determine optimal wavelet packet trees suitable for this system. They then conducted adaptive matching to each sub-band using a weighted coefficient iterative formula which has more momentum factors. Finally , a second adaptive filter of the matching results was used to acquire the fitted signal. Simulation results show that the method improves filtering performance without substantially increasing calculations.%基于离散小波变换(DWT)的自适应消噪方法为雷达信号的滤波提供了一种可行的方法.但DWT不具有平移不变性,若不用相同的小波对滤波后的信号进行重构,则会带来较大的重构误差.针对这一现象,提出了一种基于提升静态小波包变换的自适应消噪方法,它推导了静态小波包的提升实现方法,并设计出适合该系统的确定最优小波包分解树的相应步骤,利用引入更多动量因子的权系数迭代公式对各子带进行自适应匹配,并将匹配结果二次自适应,得到拟合的原信号.仿真结果表明,该方法可在计算量增加不大的前提下,进一步改善系统的滤波性能.
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。
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform
Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang
2011-08-01
Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover
Energy Technology Data Exchange (ETDEWEB)
Bradley, J.N.; Brislawn, C.M.
1992-04-11
This report describes the development of a Wavelet Vector Quantization (WVQ) image compression algorithm for fingerprint raster files. The pertinent work was performed at Los Alamos National Laboratory for the Federal Bureau of Investigation. This document describes a previously-sent package of C-language source code, referred to as LAFPC, that performs the WVQ fingerprint compression and decompression tasks. The particulars of the WVQ algorithm and the associated design procedure are detailed elsewhere; the purpose of this document is to report the results of the design algorithm for the fingerprint application and to delineate the implementation issues that are incorporated in LAFPC. Special attention is paid to the computation of the wavelet transform, the fast search algorithm used for the VQ encoding, and the entropy coding procedure used in the transmission of the source symbols.
1994-07-29
Douglas (MDA). This has been extended to the use of local SVD methods and the use of wavelet packets to provide a controlled sparsening. The goal is to be...possibilities for segmenting, compression and denoising signals and one of us (GVW) is using these wavelets to study edge sets with Prof. B. Jawerth. The
A multi-resolution wavelet algorithm for hand vein pattern recognition
Institute of Scientific and Technical Information of China (English)
Yunxin Wang; Tiegen Liu; Junfeng Jiang
2008-01-01
A novel hand vein recognition algorithm is developed based on multi-resolution wavelet analysis. The texture feature of hand vein can be extracted by three-level wavelet decomposition. Furthermore, Knearest neighbor (KNN) with support vector machines (SVM) and minimum distance classifier (MDC) are employed for feature matching. Finally, the experiments are respectively performed in identification and verification modes using Tianjin University (TJU) hand vein image database constructed by our group.The results show the feasibility and effectiveness of the proposed method.
A Low-complexity Wavelet Based Algorithm for Inter-frame Image Prediction
Directory of Open Access Journals (Sweden)
S. Usama
2002-01-01
Full Text Available In this paper, a novel multi-resolution variable block size algorithm (MRVBS is introduced. It is based on: (1 Using the wavelet components of the seven sub-bands from two layers of wavelet pyramid in the lowest resolution; (2 Performing a block matching estimation within a nine-block only in each sub-band of the lower layer; (3 Scaling the estimated motion vectors and using them as a new search center for the finest resolution. The motivation for using the multi-resolution approach is the inherent structure of the wavelet representation. A multi-resolution scheme significantly reduces the searching time, and provides a smooth motion vector field. The approach presented in this paper providing an accurate motion estimate even in the presence of single and mixed noise. As a part of this framework, a comparison of the Full search (FS algorithm, the three-step search (TSS algorithm and the new algorithm (MRVBS is presented. For a small addition in computational complexity over a simple TSS algorithm, the new algorithm achieves good results in the presence of noise.
Directory of Open Access Journals (Sweden)
A. Al-Haj
2008-01-01
Full Text Available The excellent spatial localization, frequency spread and multi-resolution characteristics of the Discrete Wavelets Transform (DWT, which were similar to the theoretical models of the human visual system, facilitated the development of many imperceptible and robust DWT-based watermarking algorithms. There had been extremely few proposed algorithms on optimized DWT-based image watermarking that can simultaneously provide perceptual transparency and robustness since these two watermarking requirements are conflicting, in this study we treat the DWT-based image watermarking problem as an optimization problem and solve it using genetic algorithms. We demonstrate through the experimental results we obtained that optimal DWT-based image watermarking can be achieved only if watermarking has been applied at specific wavelet sub-bands and by using specific watermark-amplification values.
A wavelet phase filter for emission tomography
Energy Technology Data Exchange (ETDEWEB)
Olsen, E.T.; Lin, B. [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Mathematics
1995-07-01
The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography (PET). Wavelet methods can smooth out noise while preserving significant features of images. Mallat et al. proposed a wavelet based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, the authors explore the properties of wavelet phase, with a focus on reconstruction of emission tomography images. Specifically, they show that the wavelet phase of regular Poisson noise under a Haar-type wavelet transform converges in distribution to a random variable uniformly distributed on [0, 2{pi}). They then propose three wavelet-phase-based denoising schemes which exploit this property: edge tracking, local phase variance thresholding, and scale phase variation thresholding. Some numerical results are also presented. The numerical experiments indicate that wavelet phase techniques show promise for wavelet based denoising methods.
Kim, Byung S; Yoo, Sun K
2007-09-01
The use of wireless networks bears great practical importance in instantaneous transmission of ECG signals during movement. In this paper, three typical wavelet-based ECG compression algorithms, Rajoub (RA), Embedded Zerotree Wavelet (EZ), and Wavelet Transform Higher-Order Statistics Coding (WH), were evaluated to find an appropriate ECG compression algorithm for scalable and reliable wireless tele-cardiology applications, particularly over a CDMA network. The short-term and long-term performance characteristics of the three algorithms were analyzed using normal, abnormal, and measurement noise-contaminated ECG signals from the MIT-BIH database. In addition to the processing delay measurement, compression efficiency and reconstruction sensitivity to error were also evaluated via simulation models including the noise-free channel model, random noise channel model, and CDMA channel model, as well as over an actual CDMA network currently operating in Korea. This study found that the EZ algorithm achieves the best compression efficiency within a low-noise environment, and that the WH algorithm is competitive for use in high-error environments with degraded short-term performance with abnormal or contaminated ECG signals.
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 .
Institute of Scientific and Technical Information of China (English)
陈国金; 梁军; 钱积新
2003-01-01
In industrial processes, measured data are often contaminated by noise, which causes poor performance of some techniques driven by data. Wavelet transform is a useful tool to de-noise the process information, but conventional transaction is directly employing wavelet transform to the measured variables, which will make the method less effective and more multifarious if there exists lots of process variables and collinear relationships. In this paper, a novel multivariate statistical projection analysis (MSPA) based on data de-noised with wavelet transform and blind signal analysis is presented, which can detect fault more quickly and improve the monitoring performance of the process. The simulation results applying to a double-effect evaporator verify higher effectiveness and better performance of the new MSPA than classical multivariate statistical process control(MSPC).
ASIC DESIGN OF ADAPTIVE THRESHOLD DENOISE DWT CHIP
Institute of Scientific and Technical Information of China (English)
Luo Feng; Wu Shunjun; Jiao Licheng; ZhangLinrang
2002-01-01
According to the relationship of wavelet transform and perfect reconstructive FIR filter banks, this paper presents a real-time chip with adaptive Donoho's non-linear soft-threshold for denoising in different levels of multi-scale space through rearranging the input data during convolving, filtering and sub-sampling. And more important, it gives a simple iterative algorithm to calculate the variance of the noise in interregna with no signal. It works well whether the signal or noise is stationary or not.
TRUFAS, a wavelet based algorithm for the rapid detection of planetary transits
Regulo, C; Alonso, R; Deeg, H J; Cortes, T Roca
2007-01-01
Aims: We describe a fast, robust and automatic detection algorithm, TRUFAS, and apply it to data that are being expected from the CoRoT mission. Methods: The procedure proposed for the detection of planetary transits in light curves works in two steps: 1) a continuous wavelet transformation of the detrended light curve with posterior selection of the optimum scale for transit detection, and 2) a period search in that selected wavelet transformation. The detrending of the light curves are based on Fourier filtering or a discrete wavelet transformation. TRUFAS requires the presence of at least 3 transit events in the data. Results: The proposed algorithm is shown to identify reliably and quickly the transits that had been included in a standard set of 999 light curves that simulate CoRoT data. Variations in the pre-processing of the light curves and in the selection of the scale of the wavelet transform have only little effect on TRUFAS' results. Conclusions: TRUFAS is a robust and quick transit detection algor...
A comparison of Monte Carlo dose calculation denoising techniques
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
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
Khoje, Suchitra
2017-07-24
Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit
Design methodology for optimal hardware implementation of wavelet transform domain algorithms
Johnson-Bey, Charles; Mickens, Lisa P.
2005-05-01
The work presented in this paper lays the foundation for the development of an end-to-end system design methodology for implementing wavelet domain image/video processing algorithms in hardware using Xilinx field programmable gate arrays (FPGAs). With the integration of the Xilinx System Generator toolbox, this methodology will allow algorithm developers to design and implement their code using the familiar MATLAB/Simulink development environment. By using this methodology, algorithm developers will not be required to become proficient in the intricacies of hardware design, thus reducing the design cycle and time-to-market.
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
Remote sensing image denoising by using discrete multiwavelet transform techniques
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.
Face recognition algorithm based on Gabor wavelet and locality preserving projections
Liu, Xiaojie; Shen, Lin; Fan, Honghui
2017-07-01
In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.
Optimal reorientation of underactuated spacecraft using genetic algorithm with wavelet approximation
Institute of Scientific and Technical Information of China (English)
Xinsheng Ge; Liqun Chen
2009-01-01
The optimal attitude control of an underactuated spacecraft is investigated in this paper. The flywheels of the spacecraft can somehow only provide control inputs in two independent directions. The dynamic equations are formulated for the spacecraft under a nonholonomic constraint resulting from the constant time-rate of the total angular momentum of the system. The reorientation of such underactuated spacecraft is transformed into an optimal control problem. A ggnetic algorithm is proposed to derive the control laws of the two flywheels angle velocity inputs. The control laws are approximated by the discrete orthogonal wavelets.The numerical simulations indicate that the genetic algorithm with the wavelet approximation is an effective approach to deal with the optimal reorientation of underactuated spacecraft.
Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model
Directory of Open Access Journals (Sweden)
Xia Li
2014-01-01
Full Text Available Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.
A frequency measurement algorithm for non-stationary signals by using wavelet transform
Seo, Seong-Heon; Oh, Dong Keun
2016-11-01
Scalogram is widely used to measure instantaneous frequencies of non-stationary signals. However, the basic property of the scalogram is observed only for stationary sinusoidal functions. A property of the scalogram for non-stationary signal is analytically derived in this paper. Based on the property, a new frequency measurement algorithm is proposed. In addition, a filter that can separate two similar frequency signals is developed based on the wavelet transform.
Fast algorithm of byte-to-byte wavelet transform for image compression applications
Pogrebnyak, Oleksiy B.; Sossa Azuela, Juan H.; Ramirez, Pablo M.
2002-11-01
A new fast algorithm of 2D DWT transform is presented. The algorithm operates on byte represented images and performs image transformation with the Cohen-Daubechies-Feauveau wavelet of the second order. It uses the lifting scheme for the calculations. The proposed algorithm is based on the "checkerboard" computation scheme for non-separable 2D wavelet. The problem of data extension near the image borders is resolved computing 1D Haar wavelet in the vicinity of the borders. With the checkerboard splitting, at each level of decomposition only one detail image is produced that simplify the further analysis for data compression. The calculations are rather simple, without any floating point operation allowing the implementation of the designed algorithm in fixed point DSP processors for fast, near real time processing. The proposed algorithm does not possesses perfect restoration of the processed data because of rounding that is introduced at each level of decomposition/restoration to perform operations with byte represented data. The designed algorithm was tested on different images. The criterion to estimate quantitatively the quality of the restored images was the well known PSNR. For the visual quality estimation the error maps between original and restored images were calculated. The obtained simulation results show that the visual and quantitative quality of the restored images is degraded with number of decomposition level increasing but is sufficiently high even after 6 levels. The introduced distortion are concentrated in the vicinity of high spatial activity details and are absent in the homogeneous regions. The designed algorithm can be used for image lossy compression and in noise suppression applications.
Zhai, Guangtao; Sun, Fengrong; Song, Haohao; Zhang, Mingqiang; Liu, Li; Wang, Changyu
2003-09-01
The modulus maxima of a signal's wavelet transform on different levels contain important information of the signal, which can be help to construct wavelet coefficients. A fast algorithm based on Hermite interpolation polynomial for reconstructing signal from its wavelet transform maxima is proposed in this paper. An implementation of this algorithm in medical image enhancement is also discussed. Numerical experiments have shown that compared with the Alternating Projection algorithm proposed by Mallat, this reconstruction algorithm is simpler, more efficient, and at the same time keeps high reconstruction Signal to Noise Ratio. When applied to the image contract enhancement, the computing time of this algorithm is much less compared with the one using Mallat's Alternative Projection, and the results are almost the same, so it is a practical fast reconstruction algorithm.
Institute of Scientific and Technical Information of China (English)
HU; Xingtang; ZHANG; Bing; ZHANG; Xia; ZHENG; Lanfen; TONG; Qingxi
2006-01-01
Starting with a fractal-based image-compression algorithm based on wavelet transformation for hyperspectral images, the authors were able to obtain more spectral bands with the help of of hyperspectral remote sensing. Because large amounts of data and limited bandwidth complicate the storage and transmission of data measured by TB-level bits, it is important to compress image data acquired by hyperspectral sensors such as MODIS, PHI, and OMIS; otherwise, conventional lossless compression algorithms cannot reach adequate compression ratios. Other loss-compression methods can reach high compression ratios but lack good image fidelity, especially for hyperspectral image data. Among the third generation of image compression algorithms, fractal image compression based on wavelet transformation is superior to traditional compression methods,because it has high compression ratios and good image fidelity, and requires less computing time. To keep the spectral dimension invariable, the authors compared the results of two compression algorithms based on the storage-file structures of BSQ and of BIP, and improved the HV and Quadtree partitioning and domain-range matching algorithms in order to accelerate their encode/decode efficiency. The authors' Hyperspectral Image Process and Analysis System (HIPAS) software used a VC++6.0 integrated development environment (IDE), with which good experimental results were obtained. Possible modifications of the algorithm and limitations of the method are also discussed.
Silence and Speech Segmentation for Noisy Speech Using a Wavelet Based Algorithm
Institute of Scientific and Technical Information of China (English)
MEI Xiaodan; SUN Shenghe
2001-01-01
In this paper, we present a newmethod to segment silence and speech for noisy con-dition. Conventional segmentation methods usuallylack in robustness under high background noise be-cause they are mostly dependent on amplitude or en-ergy of speech signal. For speech signal, the corre-lation between the neighbor frequency bands includ-ing most speech energy is high and little effected bynoise, but the correlation between the neighbor fre-quency bands of noise is low. So we employ the cross-correlation of neighbor sub-bands of signal to locatespeech and noise. We first performed the wavelettransform to denoise and further calculated the cross-correlation between the wavelet coefficients in two se-lected sub-bands and then used the standard deviationof cross-correlation coefficients to segment speech andnoise duration. The simulation and the result analysisshow that this method is efficient for the low-energyphonemes even in low signal-to-noise ratio, and theamount of computation is less.
Institute of Scientific and Technical Information of China (English)
袁嘉佑; 祝诗平
2016-01-01
Objective]The aim of this study was to establish the transgenic rapesee d oil identification models based on near infrared spectra,and study the effect of spectral preprocessing with wavelet denoising on the identification accuracy rate.[Method]Luhua,Jinlongyu and other six brands of bottled or barreled rapeseed oil altogether 117 samples have been already collected in earlier stage,which include 53 samples of transgene rapeseed oil and 64 samples of non-transgene rapeseed oil. The full spectrum of spectrum data of the 117 samples was collected by the FT-NIR analyzer of BRUKER company of Germany;the rapeseed oil spectral data was preprocessed by wavelet analysis,using db3 wavelet soft threshold to denoise the spectra;based on the near infrared spectral data of rapeseed oil samples,discriminant partial least squares (DPLS) were applied to set up the identification model of modified rapeseed oil.[Results]This study compared the accuracy of the modeling methods before and after the wavelet preprocessing. The accurate rate of the DPLS model increased from 96.43% to 100%.[Conclusion]The results indicated that wavelet denoising pretreatment can improve the accuracy of near infrared spectra of transgenic rapeseed oil identification model effectively.%【目的】建立基于近红外光谱的转基因菜籽油定性鉴别模型，研究小波去噪对光谱的预处理对鉴别准确率的影响。【方法】利用前期已收集的鲁花、金龙鱼等6种品牌的瓶装或桶装的菜籽油共计117份样品，其中转基因菜籽油样品53份、非转基因菜籽油样品64份，采用德国BRUKER公司的MATRIX-F型傅里叶近红外光谱仪对这些样品进行全谱段的光谱采集；利用小波分析对菜籽油光谱数据进行预处理，选用db3小波对光谱进行软阈值去噪；在菜籽油样品近红外光谱数据的基础上，采用判别偏最小二乘法（DPLS）建立转基因菜籽油定性鉴别模型。【结果】对比小波
Institute of Scientific and Technical Information of China (English)
王小兵; 孙久运; 汤海燕
2012-01-01
为了有效滤除图像高斯噪声,将数学形态学与小波域增强相结合,提出了一种高斯噪声新型滤波算法.该算法首先将噪声图像进行二维小波分解,得到低频和高频子图像;然后保留低频子图像不变,对各高频子图像根据其噪声分布特点分别设计出多角度、多结构逐级形态学滤波器进行滤波处理,并进行小波分解系数重构;最后对经过形态学滤波后的图像进行2层小波分解,通过设计出一种新型小波增强函数对不同幅值的小波系数进行不同程度的收缩处理,在此基础上进行分解系数重构.将自适应中值滤波与数学形态学滤波与本文算法进行比较,实验证明本文滤波算法其去噪效果优于前两种算法.%In order to filter the Gaussian noise in digital image,combining the Mathematical morphology and Wavelet domain enhancement,a new filter algorithm is put forward.Firstly,the noise image is conducted two-dimensional wavelet decomposition,obtaining high-frequency and low-frequency sub image.Then keep the low-frequency sub image unchanged,according to the characteristics of the Gaussian noise distribution in each high-frequency sub image,the multi-angles,multi-structure mathematical morphology filters are designed to filter out the Gaussian noise,then the wavelet coefficient are reconstructed.Finally,the image after mathematical morphology filtering are conducted two layer wavelet decomposition,a new wavelet domain enhancement function is designed so as to contract the different amplitude wavelet coefficients in different degree,then the wavelet coefficient are reconstructed.The adaptive average filter and mathematical morphology and the new filter algorithm in this paper are applied to denoising the Gaussian noise in digital image respectively,the experiment results show that the new filter algorithm in this paper is better than the others.
A WAVELET TRANSFORM BASED WATERMARKING ALGORITHM FOR PROTECTING COPYRIGHTS OF DIGITAL IMAGES
Directory of Open Access Journals (Sweden)
Divya A
2013-08-01
Full Text Available This paper proposes an algorithm of Digital Watermarking based on Biorthogonal Wavelet Transform. Digital Watermarking is a technique to protect the copyright of the multimedia data. The position of the watermark can be detected without using the original image by utilizing the correlation between the neighbours of wave co-efficient. The strength of Digital watermark is obtained according to the edge intensities resulting in good robust and Imperceptible. Results show that the proposed watermark algorithm is invisible and has good robustness against common image processing operations.
Directory of Open Access Journals (Sweden)
Burhan Ergen
2014-01-01
Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Energy Technology Data Exchange (ETDEWEB)
Jeon, Kiwan [National Institute for Mathematical Sciences, Daejeon (Korea, Republic of); Kim, Hyung Joong; Woo, Eung Je [Department of Biomedical Engineering, Kyung Hee University, Yongin (Korea, Republic of); Lee, Chang-Ock [Department of Mathematical Sciences, KAIST, Daejeon (Korea, Republic of); Seo, Jin Keun, E-mail: ejwoo@khu.ac.k [Department of Computational Science and Engineering, Yonsei University, Seoul (Korea, Republic of)
2010-12-21
Conductivity imaging based on the current-injection MRI technique has been developed in magnetic resonance electrical impedance tomography. Current injected through a pair of surface electrodes induces a magnetic flux density distribution inside an imaging object, which results in additional magnetic field inhomogeneity. We can extract phase changes related to the current injection and obtain an image of the induced magnetic flux density. Without rotating the object inside the bore, we can measure only one component B{sub z} of the magnetic flux density B = (B{sub x}, B{sub y}, B{sub z}). Based on a relation between the internal conductivity distribution and B{sub z} data subject to multiple current injections, one may reconstruct cross-sectional conductivity images. As the image reconstruction algorithm, we have been using the harmonic B{sub z} algorithm in numerous experimental studies. Performing conductivity imaging of intact animal and human subjects, we found technical difficulties that originated from the MR signal void phenomena in the local regions of bones, lungs and gas-filled tubular organs. Measured B{sub z} data inside such a problematic region contain an excessive amount of noise that deteriorates the conductivity image quality. In order to alleviate this technical problem, we applied hybrid methods incorporating ramp-preserving denoising, harmonic inpainting with isotropic diffusion and ROI imaging using the local harmonic B{sub z} algorithm. These methods allow us to produce conductivity images of intact animals with best achievable quality. We suggest guidelines to choose a hybrid method depending on the overall noise level and existence of distinct problematic regions of MR signal void.
A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations
Institute of Scientific and Technical Information of China (English)
Qianshun Chang; Xuecheng Tai; Lily Xing
2009-01-01
In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 6. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.
Directory of Open Access Journals (Sweden)
Chu Zhang
2016-02-01
Full Text Available Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874–1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT was used to reduce noises of the spectral data. Successive projections algorithm (SPA, random frog (RF and variable importance in projection (VIP were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS and a back propagation neural network (BPNN were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp of 0.911 and root mean square error of prediction (RMSEP of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging.
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.
The Application Wavelet Transform Algorithm in Testing ADC Effective Number of Bits
Directory of Open Access Journals (Sweden)
Emad A. Awada
2013-10-01
Full Text Available In evaluating Analog to Digital Convertors, many parameters are checked for performance and error rate.One of these parameters is the device Effective Number of Bits. In classical testing of Effective Number ofBits, testing is based on signal to noise components ratio (SNR, whose coefficients are driven viafrequency domain (Fourier Transform of ADC’s output signal. Such a technique is extremely sensitive tonoise and require large number of data samples. That is, longer and more complex testing process as thedevice under test increases in resolutions. Meanwhile, a new time – frequency domain approach (known asWavelet transform is proposed to measure and analyze Analog-to-Digital Converters parameter ofEffective Number of Bits with less complexity and fewer data samples.In this work, the algorithm of Wavelet transform was used to estimate worst case Effective Number of Bitsand compare the new testing results with classical testing methods. Such an algorithm, Wavelet transform,have shown DSP testing process improvement in terms of time and computations complexity based on itsspecial properties of multi-resolutions.
Study on the application of embedded zero-tree wavelet algorithm in still images compression
Zhang, Jing; Lu, Yanhe; Li, Taifu; Lei, Gang
2005-12-01
An image has directional selection capability with high frequency through wavelet transformation. It is coincident with the visual characteristics of human eyes. The most important visual characteristic in human eyes is the visual covering effect. The embedded Zero-tree Wavelet (EZW) coding method completes the same level coding for a whole image. In an image, important regions (regions of interest) and background regions (indifference regions) are coded through the same levels. On the basis of studying the human visual characteristics, that is, the visual covering effect, this paper employs an image-compressing method with regions of interest, i.e., an algorithm of Embedded Zero-tree Wavelet with Regions of Interest (EZWROI Algorism) to encode the regions of interest and regions of non-interest separately. In this way, the lost important information in the image is much less. It makes full use of channel resource and memory space, and improves the image quality in the regions of interest. Experimental study showed that a resumed image using an EZW_ROI algorithm is better in visual effects than that of EZW on condition of high compression ratio.
A data-distributed parallel algorithm for wavelet-based fusion of remote sensing images
Institute of Scientific and Technical Information of China (English)
YANG Xuejun; WANG Panfeng; DU Yunfei; ZHOU Haifang
2007-01-01
With the increasing importance of multiplatform remote sensing missions,the fast integration or fusion of digital images from disparate sources has become critical to the success of these endeavors.In this paper,to speed up the fusion process,a Data-distributed Parallel Algorithm for wavelet-based Fusion (DPAF for short) of remote sensing images which are not geo-registered remote sensing images is presented for the first time.To overcome the limitations on memory space as well as the computing capability of a single processor,data distribution,data-parallel processing and load balancing techniques are integrated into DPAF.To avoid the inherent communication overhead of a wavelet-based fusion method,a special design called redundant partitioning is used,which is inspired by the characteristics of wavelet transform.Finally,DPAF is evaluated in theory and tested on a 32-CPU cluster of workstations.The experimental results show that our algorithm has good parallel performance and scalability.
Noise reduction in LOS wind velocity of Doppler lidar using discrete wavelet analysis
Institute of Scientific and Technical Information of China (English)
Songhua Wu(吴松华); Zhishen Liu(刘智深); Dapeng Sun(孙大鹏)
2003-01-01
The line of sight (LOS) wind velocity can be determined from the incoherent Doppler lidar backscattering signals. Noise and interference in the measurement greatly degrade the inversion accuracy. In this paper,we apply the discrete wavelet denoising method by using biorthogonal wavelets and adopt a distancedependent thresholds algorithm to improve the accuracy of wind velocity measurement by incoherent Doppler lidar. The noisy simulation data are processed and compared with the true LOS wind velocity.The results are compared by the evaluation of both the standard deviation and correlation coefficient.The results suggest that wavelet denoising with distance-dependent thresholds can considerably reduce the noise and interfering turbulence for wind lidar measurement.
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。
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
Denoising of Noisy Pixels in Video by Neighborhood Correlation Filtering Algorithm
Directory of Open Access Journals (Sweden)
P.Karunakaran
2012-07-01
Full Text Available A fast filtering algorithm for color video based on Neighborhood Correlation Filtering is presented. By utilizing a 3 × 3 pixel template, the algorithm can discriminate and filter various patterns of noise spots or blocks. In contrast with many kinds of median filtering algorithm, which may cause image blurring, it has much higher edge preserving ability. Furthermore, this algorithm is able to synchronously reflect image quality via amount, location and density statistics. Filtering of detected pixels is done by NCF algorithm based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE, the peak-signal-to-noise ratio (PSNR and the normalized color difference (NCD.
A New Denoising Technique for Capillary Electrophoresis Signals
Institute of Scientific and Technical Information of China (English)
WANG,Ying(王瑛); MO,Jin-Yuan(莫金垣)
2002-01-01
Capillary electrophorsis (CE) is a powerful analytical tool in chemistry. Thus, it is valuable to solve the denoising of CE signals. A new denoising method called MWDA which employs Mexican Hat wavelet is presented. It is an efficient chemometrics technique and has been applied successfully in processing CE signals. Useful information can be extracted even from signals of S/N = 1. After denoising, the peak positions are unchanged and the relative errors of peak height are less than 3%.
A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm
Directory of Open Access Journals (Sweden)
Shi Yan
2013-06-01
Full Text Available Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data,but it is essential only in the case where the reflection coefficients of SAR scene are sparse. This paper proposed a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR image can be produced with the proposed method even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.
The Application of Compressive Sensing on Spectra De-noising
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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.
Directory of Open Access Journals (Sweden)
Quiles FJ
2007-01-01
Full Text Available We introduce and evaluate the implementations of three parallel video-sequences decorrelation algorithms. The proposed algorithms are based on the nonalternating classic three-dimensional wavelet transform (3D-WT. The parallel implementations of the algorithms are developed and tested on a shared memory system, an SGI origin 3800 supercomputer making use of a message-passing paradigm. We evaluate and analyze the performance of the implementations in terms of the response time and speed-up factor by varying the number of processors and various video coding parameters. The key points enabling the development of highly efficient implementations rely on the partitioning of the video sequences into groups of frames and a workload distribution strategy supplemented by the use of parallel I/O primitives, for better exploiting the inherent features of the application and computing platform. We also evaluate the effectiveness of our algorithms in terms of the first-order entropy.
Cross-correlation of bio-signals using continuous wavelet transform and genetic algorithm.
Sukiennik, Piotr; Białasiewicz, Jan T
2015-05-30
Continuous wavelet transform allows to obtain time-frequency representation of a signal and analyze short-lived temporal interaction of concurrent processes. That offers good localization in both time and frequency domain. Scalogram and coscalogram analysis of two signal interaction dynamics gives an indication of the cross-correlation of analyzed signals in both domains. We have used genetic algorithm with a fitness function based on signals convolution to find time delay between investigated signals. Two methods of cross-correlation are proposed: one that finds single delay for analyzed signals, and one returns a vector of delay values for each of wavelet transform sub-band center frequencies. Algorithms were implemented using MATLAB. We have extracted the data of simultaneously recorded encephalogram and arterial blood pressure and have investigated their interaction dynamics. We found time delay whose value cannot be precisely determined by scalograms and coscalogram inspection. The biomedical signals used come from MIMIC database. Cross-correlation of two complex signals is commonly performed using fast Fourier transform. It works well for signals with invariant frequency content. We have determined the time delay between analyzed signals using wavelet scalograms and we have accordingly shifted one of them, aligning associated events. Their coscalogram indicates the cross-correlation of the associated events. Introducing new methods of wavelet transform in cross-correlation analysis has proven to be beneficial to the gain of the information about process interaction. Introduced solutions could be used to reason about causality between processes and gain bigger insight regarding analyzed systems. Copyright © 2015 Elsevier B.V. All rights reserved.
Institute of Scientific and Technical Information of China (English)
牟锴钰; 韦明; 杨辉; 彭振
2015-01-01
目的：眨眼伪迹是脑电中一种常见且影响严重的伪迹。本论文提出一种基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法，无需眼电参考信号，做到自动去除单导脑电信号中的眨眼伪迹。方法首先利用小波奇异点检测特性以检测眨眼伪迹的峰值位置，然后只对眨眼伪迹区域进行小波阈值去噪。结果实验结果表明，本方法能够有效检测眨眼伪迹，避免了普通方法去噪时对非眨眼区域的影响。结论本方法使用的阈值和阈值函数优于典型的阈值和软、硬阈值函数，有效地去除了脑电中的眨眼伪迹。%Objective Blink artifact is common and has serious effect on EEG .To remove the blink artifacts in EEG automatically without a reference channel , this paper proposes a method for blink artifact removal based on wavelet singularity detection and thresholding denoising .Methods First, the detection property of wavelet singularity was used to detect the positions of blink artifact peaks , and then only the blink artifact zones were denoised by the wavelet thresholding method .Results The experimental results showed that the proposed method could effectively detect the blink artifacts and avoid affecting the EEG outside the blink artifact zones in usual methods .Conclusions The threshold and thresholding function used in the paper could effectively remove the blink artifacts in EEG and outperform the conventional soft or hard thresholding estimators.
Institute of Scientific and Technical Information of China (English)
董婵婵; 桂志国; 张权; 郝慧艳; 张芳; 刘祎; 孙未雅
2016-01-01
针对低剂量 CT （Computed Tomography）重建图像质量退化的问题，提出一种基于小波收缩和绝对差值排序各项异性扩散的 MLEM（Maximum Likelihood Expectation Maximization）低剂量 CT 重建算法。算法在每次迭代中首先采用 MLEM算法对低剂量CT 投影数据进行重建。由于各项异性扩散对噪声敏感，所以算法先对重建后的图像进行小波变换，再在更稳定的低频小波域进行基于绝对差值排序的各项异性扩散处理，对小波高频系数进行软阈值降噪处理。然后将降噪处理后的系数进行小波反变换，得到降噪后的图像。最后使用中值滤波对图像进行处理，从而消除脉冲噪声点。实验结果表明，与其他几种常用重建算法相比，该算法重建的图像信噪比更高，归一化均方误差更小，处理后的图像更清晰，即可以在抑制噪声的同时，较好地保持图像的边缘和细节信息。%Concerning the problem of quality degradation of low-dose CT reconstruction images,we presented an MLEM low-dose CT reconstruction method which is based on wavelet shrinkage and rank-ordered absolute differences anisotropic diffusion.In each time of iteration,the algorithm first uses MLEMto reconstruct the low-dose projection data.Since the anisotropic diffusion is sensitive to noises,so the algorithm performs wavelet transform on the reconstructed image prior to conducting anisotropic diffusion processing based on rank-ordered absolute differences in more stable low-frequency wavelet domain and then carries out the soft threshold denoising processing on high-frequency coefficient of wavelet.After that the algorithm performs inverse discrete wavelet transform (IDWT)on the coefficients with denoising treatment and obtains the denoised images.Finally it uses median filter to process the image so as to eliminate the impulse noise points.Experimental results showed that compared with some other common
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.
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.
MRS3D: 3D Spherical Wavelet Transform on the Sphere
Lanusse, F.; Rassat, A.; Starck, J.-L.
2011-12-01
Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. We present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We tested the 3D wavelet transform and as a toy-application, applied a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and found we can successfully remove noise without much loss to the large scale structure. The new spherical 3D isotropic wavelet transform, called MRS3D, is ideally suited to analysing and denoising future 3D spherical cosmological surveys; it uses a novel discrete spherical Fourier-Bessel Transform. MRS3D is based on two packages, IDL and Healpix and can be used only if these two packages have been installed.
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.
Applications of discrete multiwavelet techniques to image denoising
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.
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.
Qian, Jinfang; Zhang, Changjiang
2014-11-01
An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.
Huang, S. Q.; Wang, Z. L.; Xie, T. G.; Li, Z. C.
2017-09-01
Speckle noise in synthetic aperture radar (SAR) image is produced by the coherent imaging mechanism, which brings a great impact on the change information acquisition of multi-temporal SAR images. Two-dimensional stationary wavelet transform (SWT) and bi-dimensional empirical mode decomposition (BEMD) are the non-stationary signal processing theory of multi-scale transform. According to their implementation process and SAR image characteristic, this paper proposed a new multi-temporal SAR image change detection method based on the combination of the stationary wavelet transform and the bi-dimensional intrinsic mode function (BIMF) features, called SWT-BIMF algorithm. The contribution of the new algorithm includes two aspects. One is the design of the two selections of decomposition features, that is, the speckle noise filtering; another is the selected features to perform the enhance processing, so more effective change information will obtain. The feasibility of the SWT-BIMF algorithm is verified by the measured SAR image data, and good experimental results are obtained.
Directory of Open Access Journals (Sweden)
A. Sreenivasa Murthy
2014-11-01
Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.
Wavelet despiking of fractographs
Aubry, Jean-Marie; Saito, Naoki
2000-12-01
Fractographs are elevation maps of the fracture zone of some broken material. The technique employed to create these maps often introduces noise composed of positive or negative 'spikes' that must be removed before further analysis. Since the roughness of these maps contains useful information, it must be preserved. Consequently, conventional denoising techniques cannot be employed. We use continuous and discrete wavelet transforms of these images, and the properties of wavelet coefficients related to pointwise Hoelder regularity, to detect and remove the spikes.
Institute of Scientific and Technical Information of China (English)
LI,Qian-Feng; ZHANG,Xiao-Yun; ZHANG,Hong-Yi; CHEN,Xing-Guo; LIU,Man-Cang; HU,Zhi-De
2001-01-01
The electroosmotic flow mobility has been measured by the combination of monitoring the change in electric current dur ing electrophoretic run and operating the wavelet transform. Once the sample solvent zone with different ionic stenggth from background electrolyte migrated from the capillary, a sudden change in current could be observed from the ekectric current record of time history. The exact time (in the middle of abrupt range) corresponding to the abrupt change in cur rent was determined by wavelet transform. This work showed posed method was in a good agreement with the neutral mark er method commonly used.
New image compression algorithm based on improved reversible biorthogonal integer wavelet transform
Zhang, Libao; Yu, Xianchuan
2012-10-01
The low computational complexity and high coding efficiency are the most significant requirements for image compression and transmission. Reversible biorthogonal integer wavelet transform (RB-IWT) supports the low computational complexity by lifting scheme (LS) and allows both lossy and lossless decoding using a single bitstream. However, RB-IWT degrades the performances and peak signal noise ratio (PSNR) of the image coding for image compression. In this paper, a new IWT-based compression scheme based on optimal RB-IWT and improved SPECK is presented. In this new algorithm, the scaling parameter of each subband is chosen for optimizing the transform coefficient. During coding, all image coefficients are encoding using simple, efficient quadtree partitioning method. This scheme is similar to the SPECK, but the new method uses a single quadtree partitioning instead of set partitioning and octave band partitioning of original SPECK, which reduces the coding complexity. Experiment results show that the new algorithm not only obtains low computational complexity, but also provides the peak signal-noise ratio (PSNR) performance of lossy coding to be comparable to the SPIHT algorithm using RB-IWT filters, and better than the SPECK algorithm. Additionally, the new algorithm supports both efficiently lossy and lossless compression using a single bitstream. This presented algorithm is valuable for future remote sensing image compression.
Directory of Open Access Journals (Sweden)
Reza Bohlouli
2012-01-01
Full Text Available Polyethylene (PE pipelines with electrofusion (EF joining is an essential method of transportation of gas energy. EF joints are weak points for leakage and therefore, Nondestructive testing (NDT methods including ultrasonic array technology are necessary. This paper presents a practical NDT method of fusion joints of polyethylene piping using intelligent ultrasonic image processing techniques. In the proposed method, to detect the defects of electrofusion joints, the NDT is applied based on an ANN-Wavelet method as a digital image processing technique. The proposed approach includes four steps. First an ultrasonic-phased array technique is used to provide real time images of high resolution. In the second step, the images are preprocessed by digital image processing techniques for noise reduction and detection of ROI (Region of Interest. Furthermore, to make more improvement on the images, mathematical morphology techniques such as dilation and erosion are applied. In the 3rd step, a wavelet transform is used to develop a feature vector containing 3-dimensional information on various types of defects. In the final step, all the feature vectors are classified through a backpropagation-based ANN algorithm. The obtained results show that the proposed algorithms are highly reliable and also precise for NDT monitoring.
Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation
Directory of Open Access Journals (Sweden)
S.K. Saha
2015-01-01
Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.
BMW a ROSAT-HRI source catalogue obtained with a wavelet transform detection algorithm
Panzera, M R; Covino, S; Guzzo, L; Israel, G L; Lazzati, D; Mignani, R P; Moretti, A; Tagliaferri, G
2001-01-01
In collaboration with the Observatories of Palermo and Rome and the SAX-SDC we are constructing a multi-site interactive archive system featuring specific analysis tools. In this context we developed a detection algorithm based on the Wavelet Transform (WT) and performed a systematic analysis of all ROSAT-HRI public data (~3100 observations +1000 to come). The WT is specifically suited to detect and characterize extended sources while properly detecting point sources in very crowded fields. Moreover, the good angular resolution of HRI images allows the source extension and position to be accurately determined. This effort has produced the BMW (Brera Multiscale Wavelet) catalogue, with more than 19,000 sources detected at the ~4.2sigma level. For each source detection we have information on the position, X-ray flux and extension. This allows for instance to select complete samples of extended X-ray sources such as candidate clusters of galaxies or SNR's. Details about the detection algorithm and the catalogue ...
A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic
Directory of Open Access Journals (Sweden)
Meng Fan-Bo
2016-01-01
Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.
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
Self-adapting denoising, alignment and reconstruction in electron tomography in materials science
Energy Technology Data Exchange (ETDEWEB)
Printemps, Tony, E-mail: tony.printemps@cea.fr [Université Grenoble Alpes, F-38000 Grenoble (France); CEA, LETI, MINATEC Campus, F-38054 Grenoble (France); Mula, Guido [Dipartimento di Fisica, Università di Cagliari, Cittadella Universitaria, S.P. 8km 0.700, 09042 Monserrato (Italy); Sette, Daniele; Bleuet, Pierre; Delaye, Vincent; Bernier, Nicolas; Grenier, Adeline; Audoit, Guillaume; Gambacorti, Narciso; Hervé, Lionel [Université Grenoble Alpes, F-38000 Grenoble (France); CEA, LETI, MINATEC Campus, F-38054 Grenoble (France)
2016-01-15
An automatic procedure for electron tomography is presented. This procedure is adapted for specimens that can be fashioned into a needle-shaped sample and has been evaluated on inorganic samples. It consists of self-adapting denoising, automatic and accurate alignment including detection and correction of tilt axis, and 3D reconstruction. We propose the exploitation of a large amount of information of an electron tomography acquisition to achieve robust and automatic mixed Poisson–Gaussian noise parameter estimation and denoising using undecimated wavelet transforms. The alignment is made by mixing three techniques, namely (i) cross-correlations between neighboring projections, (ii) common line algorithm to get a precise shift correction in the direction of the tilt axis and (iii) intermediate reconstructions to precisely determine the tilt axis and shift correction in the direction perpendicular to that axis. Mixing alignment techniques turns out to be very efficient and fast. Significant improvements are highlighted in both simulations and real data reconstructions of porous silicon in high angle annular dark field mode and agglomerated silver nanoparticles in incoherent bright field mode. 3D reconstructions obtained with minimal user-intervention present fewer artefacts and less noise, which permits easier and more reliable segmentation and quantitative analysis. After careful sample preparation and data acquisition, the denoising procedure, alignment and reconstruction can be achieved within an hour for a 3D volume of about a hundred million voxels, which is a step toward a more routine use of electron tomography. - Highlights: • Goal: perform a reliable and user-independent 3D electron tomography reconstruction. • Proposed method: self-adapting denoising and alignment prior to 3D reconstruction. • Noise estimation and denoising are performed using wavelet transform. • Tilt axis determination is done automatically as well as projection alignment.
Wavelet applied to computer vision in astrophysics
Bijaoui, Albert; Slezak, Eric; Traina, Myriam
2004-02-01
Multiscale analyses can be provided by application wavelet transforms. For image processing purposes, we applied algorithms which imply a quasi isotropic vision. For a uniform noisy image, a wavelet coefficient W has a probability density function (PDF) p(W) which depends on the noise statistic. The PDF was determined for many statistical noises: Gauss, Poission, Rayleigh, exponential. For CCD observations, the Anscombe transform was generalized to a mixed Gasus+Poisson noise. From the discrete wavelet transform a set of significant wavelet coefficients (SSWC)is obtained. Many applications have been derived like denoising and deconvolution. Our main application is the decomposition of the image into objects, i.e the vision. At each scale an image labelling is performed in the SSWC. An interscale graph linking the fields of significant pixels is then obtained. The objects are identified using this graph. The wavelet coefficients of the tree related to a given object allow one to reconstruct its image by a classical inverse method. This vision model has been applied to astronomical images, improving the analysis of complex structures.
An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets
Directory of Open Access Journals (Sweden)
Hyunjong Cho
2014-04-01
Full Text Available With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filters are employed. As is well known, recognizing a face image with Gabor filters is a computationally heavy task. The contribution of our work is in proposing a flexible dual-stage algorithm that enables fast, hybrid face recognition. Experimental tests were performed with the Extended Yale Face Database B to verify the effectiveness and validity of the research, and we obtained better recognition results under illumination variations not only in terms of computation time but also in terms of the recognition rate in comparison to PCA- and Gabor wavelet-based recognition algorithms.
An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets
Directory of Open Access Journals (Sweden)
Hyunjong Cho
2014-04-01
Full Text Available With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filters are employed. As is well known, recognizing a face image with Gabor filters is a computationally heavy task. The contribution of our work is in proposing a flexible dual-stage algorithm that enables fast, hybrid face recognition. Experimental tests were performed with the Extended Yale Face Database B to verify the effectiveness and validity of the research, and we obtained better recognition results under illumination variations not only in terms of computation time but also in terms of the recognition rate in comparison to PCA- and Gabor wavelet-based recognition algorithms.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei
2017-07-01
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise (AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.
Institute of Scientific and Technical Information of China (English)
姚刚; 房旭民; 毛云志
2009-01-01
Frequency domain analysis of nuclear quadruple resonance (NQR) signal of ammonium nitrate (AN) via Fourier transform lacks time domain information. In this work, a threshold de-noising method, which is based on wavelet transform, for analyzing time-domain NQR signals of AN in the presence of strong background noise was proposed. The correlation coefficients between the processed signals and the signals of a standard sample were calculated, which were found to increase with the dose of AN.%针对硝铵(AN)核电四极矩共振(NQR)信号通过傅立叶变换频域分析缺乏信号时域信息的特点, 对硝铵NQR信号进行时频分析, 达到从强背景噪声下检测出NQR信号的目的. 引入小波分析阀值去噪的方法对硝铵NQR信号进行处理. 对处理后数据与标准信号之间的相关系数进行分析. 实验结果表明小波阀值去噪方法可以成功检测到硝铵的NQR信号.
基于网页结构的网页去噪算法设计%The Research and Algorithm Design of Web De-noising Technology
Institute of Scientific and Technical Information of China (English)
陈雪; 徐慧; 沈家峻
2013-01-01
本文对网页去噪的定义和分类、经典方法以及实验方法等进行了研究，在重定义启发式规则的基础上，针对文本类网页，设计新的算法，并用代码进行实现，在对比结果中验证，该算法能很好的解决文本类网页噪音问题。%This paper in the web page de-noising deifnition and classiifcation, the web page de-noising method and experimental method of classic were researched, in heavy deifnition of heuristic rules, based on the text of web pages, design new algorithm, and can realize code, in contrast to the results of veriifcation, this algorithm can be a solution to text page of the noise.
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.
A color correction algorithm for noisy multi-view images
Institute of Scientific and Technical Information of China (English)
Feng Shao; Gangyi Jiang; Mei Yu; Ken Chen
2007-01-01
A novel color correction algorithm for noisy multi-view images is presented. The key idea is to use the improved Karhunen-Loeve (K-L) transform to obtain correction matrix that can eliminate noise effect to the fullest extent. Noise variance estimation is first performed in the algorithm. In the end, wavelet transform is applied to denoise the corrected image. Experimental results show that, compared with traditional correction method, a well-performed correction result is achieved using the proposed method,and the visual effect of the denoised corrected image is almost consistent with ideal corrected image.
A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In order to enhance the image information from multi-sensor and to improve the abilities of theinformation analysis and the feature extraction, this letter proposed a new fusion approach in pixel level bymeans of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequencyband and high frequency band in higher scale. It offers a more precise method for image analysis than Wave-let Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform toobtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. ThenWPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observationde la Therre ) image into low frequency band and high frequency band in three levels. Next, two high fre-quency coefficients and low frequency coefficients of the images are combined by linear weighting strategies.Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approachcan fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM(Histogram Matched)-based fusion algorithm and WT-based fusion approach.
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.
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.
A wavelet based algorithm for DTM extraction from airborne laser scanning data
Xu, Liang; Yang, Yan; Tian, Qingjiu
2007-06-01
The automatic extraction of Digital Terrain Model (DTM) from point clouds acquired by airborne laser scanning (ALS) equipment remains a problem in ALS data filtering nowadays. Many filter algorithms have been developed to remove object points and outliers, and to extract DTM automatically. However, it is difficult to filter in areas where few points have identical morphological or geological features that can present the bare earth. Especially in sloped terrain covered by dense vegetation, points representing bare earth are often identified as noisy data below ground. To extract terrain surface in these areas, a new algorithm is proposed. First, the point clouds are cut into profiles based on a scan line segmentation algorithm. In each profile, a 1D filtering procedure is determined from the wavelet theory, which is superior in detecting high frequency discontinuities. After combining profiles from different directions, an interpolated grid data representing DTM is generated. In order to evaluate the performance of this new approach, we applied it to the data set used in the ISPRS filter test in 2003. 2 samples containing mostly vegetation on slopes have been processed by the proposed algorithm. It can be seen that it filtered most of the objects like vegetation and buildings in sloped area, and smoothed the hilly mountain to be more close to its real terrain surface.
Chebyshev and Modified Wavelet Algorithm Based Sleep Arousals Detection Using EEG Sensor Database
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Mahalaxmi U. S. B. K.
2017-04-01
Full Text Available Electroencephalographic (EEG arousals are generally observed in EEG recordings as an awakening response of the human brain. Sleep apnea is a major sleep disorder. The patients, with Severe Sleep Apnea (SAS suffers from frequent interruptions in their sleep which brings about EEG arousals. In this paper, a new method for Segmentation and Filtering process of EEG sensor database signals for finding sleep arousals using Chebyshev and Modified Wavelet Algorithm is proposed. The Segmentation Algorithm appears as various features extracted from EEG Data’s and PSG Recordings. The Chebyshev Equiripple Filter is used in Filtering algorithm and then MSVM [M-Support Vector Machine] was utilized as Classification Tool. Algorithms are performed and different features are extracted and the ROC characteristics are performed. The extracted features are Delta, Gama, Beta, Alpha, Sigma of the EEG signal, EEG Signal Mean, EEG Signal Standard Deviation, EEG Signal Peak Signal to Noise Ratio [PSNR], and EEG Signal Normalization. MSVM tool showing EEG signals results.
Independent component analysis and decision trees for ECG holter recording de-noising.
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Jakub Kuzilek
Full Text Available We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA. This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG, but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact were compared.
Directory of Open Access Journals (Sweden)
Esther A.K James
2012-01-01
Full Text Available Problem statement: The paper addresses the face recognition problem by proposing Weighted Fuzzy Fisherface (WFF technique using Biorthogonal Transformation. The Weighted Fuzzy Fisherface technique is an extension of Fisher Face technique by introducing fuzzy class membership to each training sample in calculating the scatter matrices. Approach: In weighted fuzzy fisherface method, the weight emphasizes classes that are close together and deemphasizes the classes that are far away from each other. Results: The proposed method is more advantageous for the classification task and its accuracy is improved. Also with the performance measures False Acceptance Rate (FAR, False Rejection Rate (FRR and Equal Error Rate (EER are calculated. Conclusion: Weighted fuzzy fisherface algorithm using wavelet transform can effectively and efficiently used for face recognition and its accuracy is improved.
Daniel, Ebenezer; Anitha, J
2016-04-01
Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
2010-02-01
This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.
ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm
Kora, Padmavathi; Sri Rama Krishna, K.
2016-12-01
Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.
Tikhotsky, S.; Achauer, U.; Fokin, I.
2009-04-01
A self-adaptive automated parameterisation approach is suggested for the inversion of controlled-source seismic tomography data. The velocities and interfaces are parameterized by their Haar wavelet expansion coefficients. Only those coefficients that are well constrained by the data, as measured by the number of rays that cross the corresponding wavelet function support area (hit counts) and their angular coverage, are inverted for, others are set to zero. The adequacy of the suggested empirical resolution measures are investigated on the 2D and 3D synthetic examples by the comparision with the corresponding diagonal elements of the resolution matrices. The rule for the optimal selection of algoritm parameters has been constructed. We show with the series of the synthetic tests that our approach leads to the reasonable distribution of resolution throughout the model even in cases of irregular ray coverage and helps to overcome the trade-off between different types of model parameters. The developed algorithm has been used for the construction of the Vesuvius volcano area velocity model based on the TOMOVES experiment data. The described algorithm allows to obtain the multi-resolution model that provide fine structure information in well-sampled areas and a smooth generalized pattern in other parts of the model. Layer-stripping as well as whole-model approaches were applied to the same data set in order to test the stability of the inversion results. Key features of the model (high-velocity body at depth's -1.2 - 1.0 km under the volcano edifice and a low-velocity volcano root in the carbonate basement, low-velocity basins at the volcano flanks and general position of the carbonate basement top at 1-2 km depth) remain stable regardless of the inversion approach used. Our model well agrees with the previous studies particularly in the structure of the upper volcano-sedimentary layer but provides more fine details and reveals additional structures at greater depth's.
Wavelet-Based Speech Enhancement Using Time-Frequency Adaptation
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Kun-Ching Wang
2009-01-01
Full Text Available Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient threshold (WCT based on time-frequency adaptation is proposed. In addition, an unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. The wavelet coefficient threshold (WCT of each subband is first temporally adjusted according to the value of a posterior signal-to-noise ratio (SNR. To prevent the degradation of unvoiced sounds during noise, the algorithm utilizes a simple speech/noise detector (SND and further divides speech signal into unvoiced and voiced sounds. Then, we apply appropriate wavelet thresholding according to voiced/unvoiced (V/U decision. Based on the masking properties of human auditory system, a perceptual gain factor is adopted into wavelet thresholding for suppressing musical residual noise. Simulation results show that the proposed method is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods.
Wavelet and wavelet packet compression of electrocardiograms.
Hilton, M L
1997-05-01
Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.
Denoising algorithm for FOG based on adaptive time-frequency peak filtering%基于自适应时频峰值滤波的光纤陀螺去噪算法
Institute of Scientific and Technical Information of China (English)
顾姗姗; 刘建业; 曾庆化; 陈维娜; 陈磊江
2014-01-01
An algorithm based on adaptive time-frequency peak filtering(ATFPF) for fiber optical gyro(FOG) is proposed to reduce the noise of FOG and improve the precision of the inertial navigation system. With the presented algorithm, the FOG signal is transformed and modulated, and then time frequency analysis of the modulated signal is made by pseudo Wigner-Ville distribution(PWVD). A rule for the optimal window length selection of adaptive PWVD is given, and the instantaneous frequency of the coded signal is estimated by local peak search. In this way, the useful signal is restored and the noise of FOG is reduced. Simulation and real data are processed by discrete wavelet transform(DWT) and ATFPF algorithms separately, and the results show that proposed algorithm can reduce the noise of FOG effectively, and the improvement of SNR is 1~3 dB. The signal denoised by ATFPF can effectively track the initial signal, especially for the high dynamic signal.%为减小光纤陀螺输出信号噪声、提高惯导系统精度，提出了光纤陀螺信号自适应时频峰值滤波算法。对光纤陀螺信号进行初始变换并调制，采用伪 Wigner-Ville 分布对调制信号进行时频分析，给出了一种自适应的伪 Wigner-Ville 分布最优窗长获取准则，通过局部峰值搜索实现编码信号的瞬时频率估计进而还原出有用信号，实现了光纤陀螺噪声的去除。详细对比了小波方法与自适应时频峰值滤波算法并分析了两者的去噪效果。仿真结果和实际数据验证表明：自适应时频峰值滤波算法能有效减小光纤陀螺输出噪声，信噪比比小波滤波改善1~3 dB；特别对于高动态信号，该算法滤波后的信号能够有效地跟踪原始信号。
An Improved Brain Tumour Classification System using Wavelet Transform and Neural Network.
Dhas, DAS; Madheswaran, M
2015-06-09
An improved brain tumour classification system using wavelet transform and neural network is developed and presented in this paper. The anisotropic diffusion filter is used for image denoising and the performance of oriented rician noise reducing anisotropic diffusion (ORNRAD) filter is validated. The segmentation of the denoised image is carried out by Fuzzy C-means clustering. The features are extracted using Symlet and Coiflet Wavelet transform and Levenberg Marquardt algorithm based neural network is used to classify the magnetic resonance imaging (MRI) images. This MRI classification technique is tested and analysed with the existing methodologies and its performance is found to be satisfactory with a classification accuracy of 93.02%. The developed system can assist the physicians for classifying the MRI images for better decision-making.
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Jamal Salahaldeen Majeed Alneamy
2014-01-01
Full Text Available Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO algorithm and fuzzy wavelet neural network (FWNN for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI machine learning repository. The performance of the proposed method (TLBO_FWNN is estimated using K-fold cross validation based on mean square error (MSE, classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.
基于LabVIEW的超声信号小波阈值去噪方法%Wavelet Threshold De-Noising Method of Ultrasonic Signal Based on LabVIEW
Institute of Scientific and Technical Information of China (English)
张燕; 周西峰; 郭前岗
2012-01-01
图形化编程软件LabVIEW具有强大的信号采集功能,但对于需要进行大量数据运算处理的复杂应用就显得力不从心.数学工具软件MATLAB强大的计算功能及较高的编程效率可以弥补LabVIEW开发的不足.文中结合两者软件的优点,采用混合编程的方法进行实际工程的去噪处理,同时针对超声缺陷回波信号信噪比低、易于被噪声淹没的特点,选取小波阈值去噪的方法,通过调用LabVIEW里的MATLAB Script节点对信号进行仿真实验,仿真结果表明采用stein的无偏风险阈值去噪的方法消噪效果最为明显,得到较高的信噪比.%Graphical programming software LabVIEW has a very strong signal acquisition function,but to process large amounts of data computing for complex applications.it becomes powerless. Mathematical tool software MATLAB with powerful computing and higher programming efficiency can make up for the lack of LabVIEW development. Absorbing the advantages of the two software, use the hybrid programming approach to practical engineering de-noising. At the same time,signal to noise ratio for ultrasonic flaw echo signal is low, and the signal is easy to be drowned by the noise. Owe to the characteristics,select the method of wavelet threshold,by calling the MAT-LAB Script node for signal simulation. Simulation result shows that the stein's unbiased risk threshold method can get better de-noising effect,and it gets a higher signal to noise ratio.
Quantum Boolean image denoising
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.
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Yan Sun
2017-06-01
Full Text Available This paper puts forward a new color multi-focus image fusion algorithm based on fuzzy theory and dual-tree complex wavelet transform for the purpose of removing uncertainty when choosing sub-band coefficients in the smooth regions. Luminance component is the weighted average of the three color channels in the IHS color space and it is not sensitive to noise. According to the characteristics, luminance component was chosen as the measurement to calculate the focus degree. After separating the luminance component and spectrum component, Fisher classification and fuzzy theory were chosen as the fusion rules to conduct the choice of the coefficients after the dual-tree complex wavelet transform. So fusion color image could keep the natural color information as much as possible. This method could solve the problem of color distortion in the traditional algorithms. According to the simulation results, the proposed algorithm obtained better visual effects and objective quantitative indicators.
A Two-dimensional Genetic Algorithm Based on the Eno-Haar Wavelet Transform%一种基于Eno-Haar小波变换二维遗传算法
Institute of Scientific and Technical Information of China (English)
宋锦萍; 赵晨萍; 李登峰
2007-01-01
A two-dimensional genetic algorithm of wavelet coefficient is presented by using the ENO wavelet transform and the decomposed characterization of the two-dimensional Haar wavelet. And simulated by the ENO interpolation the article shows the affectivity and the superiority of this algorithm.
Exact reconstruction with directional wavelets on the sphere
Wiaux, Y.; McEwen, J. D.; Vandergheynst, P.; Blanc, O.
2008-08-01
A new formalism is derived for the analysis and exact reconstruction of band-limited signals on the sphere with directional wavelets. It represents an evolution of a previously developed wavelet formalism developed by Antoine & Vandergheynst and Wiaux et al. The translations of the wavelets at any point on the sphere and their proper rotations are still defined through the continuous three-dimensional rotations. The dilations of the wavelets are directly defined in harmonic space through a new kernel dilation, which is a modification of an existing harmonic dilation. A family of factorized steerable functions with compact harmonic support which are suitable for this kernel dilation are first identified. A scale-discretized wavelet formalism is then derived, relying on this dilation. The discrete nature of the analysis scales allows the exact reconstruction of band-limited signals. A corresponding exact multi-resolution algorithm is finally described and an implementation is tested. The formalism is of interest notably for the denoising or the deconvolution of signals on the sphere with a sparse expansion in wavelets. In astrophysics, it finds a particular application for the identification of localized directional features in the cosmic microwave background data, such as the imprint of topological defects, in particular, cosmic strings, and for their reconstruction after separation from the other signal components.
Analysis of phonocardiogram signals using wavelet transform.
Meziani, F; Debbal, S M; Atbi, A
2012-08-01
Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal.
Discrete wavelet transformations an elementary approach with applications
Van Fleet, Patrick
2008-01-01
An "applications first" approach to discrete wavelet transformations. Discrete Wavelet Transformations provides readers with a broad elementary introduction to discrete wavelet transformations and their applications. With extensive graphical displays, this self-contained book integrates concepts from calculus and linear algebra into the construction of wavelet transformations and their various applications, including data compression, edge detection in images, and signal and image denoising. The book begins with a cursory look at wavelet transformation development and illustrates its
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Yahya AL-Nabhani
2015-10-01
Full Text Available Digital watermarking, which has been proven effective for protecting digital data, has recently gained considerable research interest. This study aims to develop an enhanced technique for producing watermarked images with high invisibility. During extraction, watermarks can be successfully extracted without the need for the original image. We have developed discrete wavelet transform with a Haar filter to embed a binary watermark image in selected coefficient blocks. A probabilistic neural network is used to extract the watermark image. To evaluate the efficiency of the algorithm and the quality of the extracted watermark images, we used widely known image quality function measurements, such as peak signal-to-noise ratio (PSNR and normalized cross correlation (NCC. Results indicate the excellent invisibility of the extracted watermark image (PSNR = 68.27 dB, as well as exceptional watermark extraction (NCC = 0.9779. Experimental results reveal that the proposed watermarking algorithm yields watermarked images with superior imperceptibility and robustness to common attacks, such as JPEG compression, rotation, Gaussian noise, cropping, and median filter.
A novel super-resolution image fusion algorithm based on improved PCNN and wavelet transform
Liu, Na; Gao, Kun; Song, Yajun; Ni, Guoqiang
2009-10-01
Super-resolution reconstruction technology is to explore new information between the under-sampling image series obtained from the same scene and to achieve the high-resolution picture through image fusion in sub-pixel level. The traditional super-resolution fusion methods for sub-sampling images need motion estimation and motion interpolation and construct multi-resolution pyramid to obtain high-resolution, yet the function of the human beings' visual features are ignored. In this paper, a novel resolution reconstruction for under-sampling images of static scene based on the human vision model is considered by introducing PCNN (Pulse Coupled Neural Network) model, which simplifies and improves the input model, internal behavior and control parameters selection. The proposed super-resolution image fusion algorithm based on PCNN-wavelet is aimed at the down-sampling image series in a static scene. And on the basis of keeping the original features, we introduce Relief Filter(RF) to the control and judge segment to overcome the effect of random factors(such as noise, etc) effectively to achieve the aim that highlighting interested object though the fusion. Numerical simulations show that the new algorithm has the better performance in retaining more details and keeping high resolution.
Institute of Scientific and Technical Information of China (English)
王玉田; 杨哲; 侯培国; 程朋飞; 曹丽芳
2016-01-01
The use of the mineral oil is an important cause of air pollution such as fog .The effectiveness and rapidity of the de‐noising processing in mineral oil fluorescence spectroscopy detection system is a hot issue of the online real‐time monitoring sys‐tem .The de‐noising method of the lifting wavelet transform (LWT ) in the application of mineral oil fluorescence spectrum is proposed .Compared with traditional discrete wavelet transform (DWT) ,this wavelet transform method decomposes the existing wavelet filter module into the basic construction modules and steps to complete the transform with simplicity and a fast speed . There are characteristics of low computational complexity ,in situ operation and the easy implement in the denoising process of mineral oil fluorescence spectra .The LWT can effectively solve the problems in these respects .The three methods of LWT , DWT and EMD are applied to the fluorescence spectra of 0# diesel oil ,97# gasoline and kerosene .The indicators evaluating de‐noising effect such as the Signal‐to‐Noise Ratio (SNR) ,Mean Squared Error (MSE) and Normalied Correlation Coefficient (NCC) of the three kinds of mineral oil in the fluorescence spectra denoising prove the effectiveness of the lifting scheme wavelet transform in the application of mineral oil fluorescence spectrum .Meanwhile ,the lifting scheme transform can improve the flexi‐bility of structure and operation simplicity that makes the de‐noising time reduced by 62% ,validating the speediness of the de‐noising method of the LWT in the application of mineral oil fluorescence spectrum and it is suitable for mineral oil fast de‐noising processing system in real time .%矿物油的使用是造成雾霾等空气污染问题的重要原因。矿物油荧光光谱检测系统光谱消噪处理的有效性和快速性是在线实时监测系统的热点问题。研究应用提升算法小波变换（LWT ）矿物油荧光光谱去噪的方法。与传
Study of Denoising Method of Images- A Review
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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
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Jian-feng Zhao
2017-01-01
Full Text Available This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks.
CW-THz image contrast enhancement using wavelet transform and Retinex
Chen, Lin; Zhang, Min; Hu, Qi-fan; Huang, Ying-Xue; Liang, Hua-Wei
2015-10-01
To enhance continuous wave terahertz (CW-THz) scanning images contrast and denoising, a method based on wavelet transform and Retinex theory was proposed. In this paper, the factors affecting the quality of CW-THz images were analysed. Second, an approach of combination of the discrete wavelet transform (DWT) and a designed nonlinear function in wavelet domain for the purpose of contrast enhancing was applied. Then, we combine the Retinex algorithm for further contrast enhancement. To evaluate the effectiveness of the proposed method in qualitative and quantitative, it was compared with the adaptive histogram equalization method, the homomorphic filtering method and the SSR(Single-Scale-Retinex) method. Experimental results demonstrated that the presented algorithm can effectively enhance the contrast of CW-THZ image and obtain better visual effect.
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Tásia Hickmann
2015-11-01
Full Text Available In this paper, an iterative forecasting methodology for time series prediction that integrates wavelet de-noising and decomposition with an Artificial Neural Network (ANN and Bootstrap methods is put forward here. Basically, a given time series to be forecasted is initially decomposed into trend and noise (wavelet components by using a wavelet de-noising algorithm. Both trend and noise components are then further decomposed by means of a wavelet decomposition method producing orthonormal Wavelet Components (WCs for each one. Each WC is separately modelled through an ANN in order to provide both in-sample and out-of-sample forecasts. At each time t, the respective forecasts of the WCs of the trend and noise components are simply added to produce the in-sample and out-of-sample forecasts of the underlying time series. Finally, out-of-sample predictive densities are empirically simulated by the Bootstrap sampler and the confidence intervals are then yielded, considering some level of credibility. The proposed methodology, when applied to the well-known Canadian lynx data that exhibit non-linearity and non-Gaussian properties, has outperformed other methods traditionally used to forecast it.
Hortos, William S.
2008-04-01
Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at
改进的双链量子遗传算法在图像去噪中的应用%Improved quantum genetic algorithm with double chains in image denoising
Institute of Scientific and Technical Information of China (English)
国强; 孙宇枭
2016-01-01
针对传统双链量子遗传算法收敛速度慢、搜索精度低、鲁棒性差等不足，提出一种F型双链量子遗传算法（ F＿DCQ⁃GA）。对编码空间进行单值映射处理，在保证量子种群适应度值与相应幅角排序单调性的前提下，缩小算法的搜索空间，增加搜索密度；在量子更新时引入自适应步长因子，使步长随目标函数在搜索点处梯度的变化而变化，有效解决了传统寻优算法普遍存在的全局最优解搜索困难的问题；在染色体变异更新时提出了π／6门，克服了原来非门变异无法更新量子比特概率幅的缺点。将F＿DCQGA优化算法应用于小波阈值去噪的阈值选择机制中，通过仿真证明F＿DCQGA优化算法提高了小波阈值函数的收敛速度和搜索精度，在图像边缘特征提取中可以获得更小的均方误差（ SME ）和更大的峰值信噪比（ RPSN ），同时又保留了大部分高频信息。%To solve the problems of slow convergence speed, low search precision and poor robustness in traditional double chains quantum genetic algorithm, a new double chains quantum genetic algorithm ( F_DCQGA ) is proposed. The coding space is mapped to reduce the algorithm searching space and increases searching density, under the premise of guaranteeing quantum population adaptation and argument population monotonicity. The adaptive step⁃length factor is introduced to the quantum updating, which changes the step⁃length with gradient of objective function in searching points. This could solve the global optimal solution search difficulties caused by oscillatory occurrence in traditional optimization algorithm. Quantumπ/6 gate is presented in chromosome mutation upadating, to overcome the shortcoming that NOT gate can not update quantum bit probability amplitude. The F_DCQGA is applied to the threshold selection of wavelet threshold denoising. Simulation results show that F_DCQGA improves the
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In the research of path planning for manipulators with many DOF, generally there is a problem in most traditional methods, which is that their computational cost (time and memory space) increases exponentially as DOF or resolution of the discrete configuration space increases. So this paper presents the collision-free trajectory planning for the space robot to capture a target based on the wavelet interpolation algorithm. We made wavelet sample on the desired trajectory of the manipulator' s end-effector to do trajectory planning by use of the proposed wavelet interpolation formula, and then derived joint vectors from the trajectory information of the endeffector based on the fixed-attitude-restrained generalized Jacobian matrix of multi-arm coordinated motion, so as to control the manipulator to capture a static body along the desired collision-free trajectory. The method overcomes the shortcomings of the typical methods, and the desired trajectory of the end-effector can be any kind of complex nonlinear curve. The algorithm is simple and highly effective and the real trajectory is close to the desired trajectory. In simulation, the planar dual-arm three DOF space robot is used to demonstrate the proposed method, and it shows that the algorithm is feasible.
A New Denoising Technique for Capillary Electrophoresis Signals
Institute of Scientific and Technical Information of China (English)
王瑛; 莫金垣
2002-01-01
Capillary electrophoresis(CE) is a powerful analytical tool in chemistry,Thus,it is valuable to solve the denoising of CE signals.A new denoising method called MWDA which emplosy Mexican Hat wavelet is presented ,It is an efficient chemometrics technique and has been applied successfully in processing CE signals ,Useful information can be extractred even from signals of S/N=1 .After denoising,the peak positions are unchanged and the relative errors of peak height are less than 3%.
An image denoising application using shearlets
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.
Directory of Open Access Journals (Sweden)
Saddaf Rubab
2015-01-01
Full Text Available Steganography is a means to hide the existence of information exchange. Using this technique the sender embeds the secret information in some other media. This is done by replacing useless data in ordinary computer files with some other secret information. The secret information could be simple text, encoded text or images. The media used as the embedding plane could be an image, audio, video or text files. Using steganography ensures that no one apart from the sender and the receiver knows about the existence of the message. In this paper, a steganography method based on transforms used i.e. Wavelet and Contourlet. Devised algorithm was used against each transform. Blowfish Encryption method is also embedded to double the security impact. The major advantage of applying transforms is that the image quality is not degraded even if the number of embedded characters is increased. The proposed system operates well in most of the test cases. The average payload capacity is also considerably high.
A wavelet-based PWTD algorithm-accelerated time domain surface integral equation solver
Liu, Yang
2015-10-26
© 2015 IEEE. The multilevel plane-wave time-domain (PWTD) algorithm allows for fast and accurate analysis of transient scattering from, and radiation by, electrically large and complex structures. When used in tandem with marching-on-in-time (MOT)-based surface integral equation (SIE) solvers, it reduces the computational and memory costs of transient analysis from equation and equation to equation and equation, respectively, where Nt and Ns denote the number of temporal and spatial unknowns (Ergin et al., IEEE Trans. Antennas Mag., 41, 39-52, 1999). In the past, PWTD-accelerated MOT-SIE solvers have been applied to transient problems involving half million spatial unknowns (Shanker et al., IEEE Trans. Antennas Propag., 51, 628-641, 2003). Recently, a scalable parallel PWTD-accelerated MOT-SIE solver that leverages a hiearchical parallelization strategy has been developed and successfully applied to the transient problems involving ten million spatial unknowns (Liu et. al., in URSI Digest, 2013). We further enhanced the capabilities of this solver by implementing a compression scheme based on local cosine wavelet bases (LCBs) that exploits the sparsity in the temporal dimension (Liu et. al., in URSI Digest, 2014). Specifically, the LCB compression scheme was used to reduce the memory requirement of the PWTD ray data and computational cost of operations in the PWTD translation stage.
A Real-Time De-Noising Algorithm for E-Noses in a Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Yi Chai
2009-02-01
Full Text Available A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors.
Output-Only Identification of System Parameters from Noisy Measurements by Multiwavelet Denoising
2014-01-01
In this paper we estimate the parameters of a multidimensional system from a record of noisy output measurements by using a multiwavelet denoising technique. In this output-only identification scheme, we extend wavelet denoising methods to the multiwavelet case. After the noise has been removed from the output records by wavelet methods, either full model identification or deterministic subspace identification can be performed. In the former case, full information on the system such as modal ...
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.
The Discrete Wavelet Transform
1991-06-01
focuses on bringing together two separately motivated implementations of the wavelet transform , the algorithm a trous and Mallat’s multiresolution...decomposition. These algorithms are special cases of a single filter bank structure, the discrete wavelet transform , the behavior of which is governed by...nonorthogonal multiresolution algorithm for which the discrete wavelet transform is exact. Moreover, we show that the commonly used Lagrange a trous
Enhancement of the automatic onset time picking via wavelet thresholding
Gaci, Said
2013-04-01
Since arrival time-picking is a critical step in the analysis of geophysical data, many time picking algorithms have been developed. Nowadays, the ''short-time-average through long-time-average trigger'' (STA/LTA) in different forms are the most commonly used. This study aims at improving this algorithm in the presence of high amplitude noise. The suggested method consists of denoising the seismic trace using the discrete wavelet transform. Therefore, the STA/LTA curve obtained from the denoised trace displays a faster build up at the position of the wave arrival, and the picking error is reduced. The application of this technique is first demonstrated on synthetic seismic traces with varying noise levels, then extended to uphole seismic traces recorded in the Algerian Sahara. The results show that the picked first arrivals are more accurate than those yielded by the standard STA/LTA algorithm and this method can tolerate high noise levels. Keywords: picking, first arrival, seismic wave, wavelet thresholding.
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.
Simultaneous denoising and compression of multispectral images
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.
Wavelet transform of neural spike trains
Kim, Youngtae; Jung, Min Whan; Kim, Yunbok
2000-02-01
Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.
Directory of Open Access Journals (Sweden)
S. Sakthivel
2011-01-01
Full Text Available Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.
Adaptive Denoising Algorithm Based on the Variance Characteristics of EMD%基于 EMD 方差特性的混沌信号自适应去噪算法
Institute of Scientific and Technical Information of China (English)
张强; 行鸿彦
2015-01-01
本文利用经验模态分解算法（EMD ），研究了不同状态下混沌信号的方差特性，提出了一种EMD分解层数自适应的去噪算法．该算法根据固有模态函数（IMF）方差最大值对应层数与总分解层数的关系，能够自适应选择需处理的IMF层数，并结合提升小波在更新和预测方面的优势综合去噪，分别以Lorenz、Chen系统（加入10％－100％的高斯白噪声）和实测的IPIX雷达数据作为混沌背景噪声进行了实验研究．结果表明：在不同程度的低噪声（≤30％）环境下，与传统小波阈值去噪等方法相比，其均方误差降低了30％以上，信噪比提高了1．5 db－3．5 db ，并能有效地去除海杂波噪声，提高混沌背景下的微弱信号检测效果．%This paper studies the variance characteristics of chaotic signal in different conditions and puts forward an adaptive denoising algorithm on account of EMD decomposition layers ,by using the Empirical Mode Decomposition (EMD ) .The arithmetic can adaptively select the IMF layer which needs to be processed ,based on the relationship between the maximum variance corre-sponding layers and the total number of decomposition layers of intrinsic mode function (IMF ) ,and it also can make intergrated denosing by making use of the lifting wavelet’ s advantages in the field of updating and predicting .It carried out the experimental study ,based on the chaotic background noise from Lorenz and Chen System (adding 10% -100% white gaussian noise ) and the measured IPIX radar data .The result shows that:under varying degrees of low noise (≤30% ) ,it decreases the error of mean square by at least 30% compared with the methods such as traditional wavelet threshold denoising ,and the signal to noise ratio has increased by 1 .5db-3 .5db ,and can effectively reduce the sea clutter noise to increase the detection effect under the background of chaos .
Institute of Scientific and Technical Information of China (English)
王玉田; 程朋飞; 侯培国; 杨哲
2015-01-01
Fluorescence analysis is an important means of detecting mineral oil in water pollutants because of high sensitivity ,se-lectivity ,ease of design ,etc .Noise generated from Photo detector will affect the sensitivity of fluorescence detection system ,so the elimination of fluorescence signal noise has been a hot issue .For the fluorescence signal ,due to the length increase of the branch set ,it produces some boundary issues .The dbN wavelet family can flexibly balance the border issues ,retain the useful signals and get rid of noise ,the de-noising effects of dbN families are compared ,the db7 wavelet is chosen as the optimal wave-let .The noisy fluorescence signal is statically decomposed into 5 levels via db7 wavelet ,and the thresholds are chosen adaptively based on the wavelet entropy theory .The pure fluorescence signal is obtained after the approximation coefficients and detail coef-ficients quantified by thresholds reconstructed .Compared with the DWT ,the signal de-noised via SWT has the advantage of in-formation integrity and time translation invariance .%荧光分析法具有灵敏度高、选择性好、易于设计等优点，是检测水中油类污染物的重要手段。光电探测器产生的噪声会影响荧光检测系统的灵敏度，荧光信号的噪声消除一直是研究的热点问题。由于荧光信号增加了支集长度，dbN族小波能够解决信号的边界问题，通过比较dbN族不同小波基的去噪效果，选择d b7为最优小波基，对含噪荧光信号作5层静态小波分解。根据小波熵理论自适应地选择阈值，高频系数经过阈值量化并重构得到纯净的荧光信号。与离散小波变换相比，静态小波变换去噪后信号具有信息完整性和时移不变性。
Institute of Scientific and Technical Information of China (English)
张晓辉; 余宁梅; 习岗; 孟晓丽
2011-01-01
The paper studies the basic characteristics and the changing laws of aloe electrical signals under different temperatures based on wavelet soft threshold de-noising method and Fast Fourier Transform. The spectral edge frequency ( SEF) , spectral gravity frequency ( SGF) and power spectral entropy ( PSE ) of plant electrical signals are used to study the changes of power spectrum of aloe electrical signals under different temperatures. The results show that the magnitude of aloe electrical signal is a strength of mV, and the frequency is below 5 Hz. The SEF and SGF in aloe leaves move to the high frequency as the temperature increases, and the PSE of the electrical signal has a dramatic increase. The study reveals that the SEF, SGF and PSE have the consistent trend to change and there is a significant relevance between PSE and SGF during the process of raising temperature. It is considered that the changes of the PSE and SGF in aloe leaves can be used as sensitive index of external environment change in leaf cells, and then implement scientific regulators of physiological and biochemical process of plant growth and development.%利用小波软阈值消噪法和快速傅里叶变换研究不同温度条件下芦荟叶片电信号的基本特征及变化规律.通过植物电信号谱边缘频率(SEF)、谱重心频率(SGF)和功率谱熵(PSE)研究不同温度下芦荟(Aloe vera L.)叶片电信号功率谱的变化.结果表明,芦荟的电信号是一种强度为mV数量级、频率分布在5 Hz以下的低频信号；随着温度的升高,电信号的SEF和SGF向高频段移动,细胞活动受到激发,PSE急剧增加；在升温过程中SEF、SGF和PSE三者的变化趋势趋于一致,PSE与SGF的变化之间有很强的关联性,因而植物电信号PSE或SGF的变化可以作为叶片细胞响应外界环境变化的灵敏指标,而对植物生长发育的生理生化过程实施科学调控.
Sharma, K. K.; Jain, Heena
2013-01-01
The security of digital data including images has attracted more attention recently, and many different image encryption methods have been proposed in the literature for this purpose. In this paper, a new image encryption method using wavelet packet decomposition and discrete linear canonical transform is proposed. The use of wavelet packet decomposition and DLCT increases the key size significantly making the encryption more robust. Simulation results of the proposed technique are also presented.
A wavelet-based ECG delineation algorithm for 32-bit integer online processing
Directory of Open Access Journals (Sweden)
Chiari Lorenzo
2011-04-01
Full Text Available Abstract Background Since the first well-known electrocardiogram (ECG delineator based on Wavelet Transform (WT presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS or floating point algebra, which are computationally demanding. Methods This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. Results The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats. The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. Conclusions The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.
Ben Zakour, Sihem; Taleb, Hassen
2016-06-01
Endpoint detection (EPD) is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done on the right way. It is truly a crucial part of supplying repeatable effects in every single wafer. When the film to be etched has been completely erased, the endpoint is reached. In order to ensure the desired device performance on the produced integrated circuit, many sensors are used to detect the endpoint, such as the optical, electrical, acoustical/vibrational, thermal, and frictional. But, except the optical sensor, the other ones show their weaknesses due to the environmental conditions which affect the exactness of reaching endpoint. Unfortunately, some exposed area to the film to be etched is very low (signal and showing the incapacity of the traditional endpoint detection method to determine the wind-up of the etch process. This work has provided a means to improve the endpoint detection sensitivity by collecting a huge numbers of full spectral data containing 1201 spectra for each run, then a new unsophisticated algorithm is proposed to select the important endpoint traces named shift endpoint trace selection (SETS). Then, a sensitivity analysis of linear methods named principal component analysis (PCA) and factor analysis (FA), and the nonlinear method called wavelet analysis (WA) for both approximation and details will be studied to compare performances of the methods mentioned above. The signal to noise ratio (SNR) is not only computed based on the main etch (ME) period but also the over etch (OE) period. Moreover, a new unused statistic for EPD, coefficient of variation (CV), is proposed to reach the endpoint in plasma etches process.
Energy Technology Data Exchange (ETDEWEB)
Eldin, A.A. Hossam; Refaey, M.A. [Electrical Engineering Department, Alexandria University, Alexandria (Egypt)
2011-01-15
This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents. (author)
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.
Random seismic noise attenuation using the Wavelet Transform
Aliouane, L.; Ouadfeul, S.; Boudella, A.; Eladj, S.
2012-04-01
In this paper we propose a technique of random noises attenuation from seismic data using the discrete and continuous wavelet transforms. Firstly the discrete wavelet transform (DWT) is applied to denoise seismic data. This last is based on the threshold method applied at the modulus of the DWT. After we calculate the continuous wavelet transform of the denoised seismic seismogram, the final denoised seismic seismogram is the continuous wavelet transform coefficients at the low scale. Application at a synthetic seismic seismogram shows the robustness of the proposed tool for random noises attenuation. We have applied this idea at a real seismic data of a vertical seismic profile realized in Algeria. Keywords: Seismic data, denoising, DWT, CWT, random noise.
基于小波消噪法的河南省农民收入增长周期分析%The Analysis of Farmers’ Income Growth Cycle Based on Wavelet Denoising Method
Institute of Scientific and Technical Information of China (English)
白晓玉
2014-01-01
针对非平稳农民收入时间序列，采用dmey离散小波进行多层分解，应用Penalize类型软阀值分别对各层的高频分量进行消噪处理，经离散小波逆变换重构得到农民收入时序的趋势成分和周期成分。改革开放以来，农民收入分别以1984、1999两个波峰年以及1992、2006两个波谷年份为分界点，可划分为三个增长周期。农民收入在不同尺度上存在着持续时间分别为3年、8年、15年的短、中、中长周期波动，中长周期表现最为明显，居于主导地位，中周期的影响主要在近十年逐步变得清晰。无论是从收入增长的周期波动还是长期趋势角度看，今后一段时期内的农民增收形势都相对乐观。千方百计继续保持农民收入快速增长的长期趋势，应是今后的工作重点。%For non-stationary time series of farmers’ income, this paper used dmey discrete wavelet to do multiple layers of decom-position, applied Penalize type soft threshold for high-frequency component of denoising of every layer. Clearly, discrete inverse wavelet transform reconstruction could reconstructed the timing of the trend component of farmers ’ income and cyclical components. Since the reform and opening up, farmers’ income growth cycle can be divided into three, they were two peaks respectively in 1984, 1999 and 1992,2006 two years as a demarcation point troughs. Farmers’ income at different scales existed duration of 3 years, 8 years, respectively, 15 years of short, medium and long-cycle fluctuations. Among them, the most obvious is long period. And what is more, the main impact of the cycle gradually become clear in recent years. Therefore, both the revenue growth from cyclical fluctuations or long-term trend perspective, farmers’ income situation in the coming period are relatively optimistic. Do everything possible to maintain the income of farmers long-term trend of rapid growth should be the focus of