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Nonlinear denoising of transient signals with application to event related potentials  [PDF]
A. Effern,K. Lehnertz,T. Schreiber,T. Grunwald,P. David,C. E. Elger
Physics , 2000, DOI: 10.1016/S1386-9477(00)00111-9
Abstract: We present a new wavelet based method for the denoising of {\it event related potentials} ERPs), employing techniques recently developed for the paradigm of deterministic chaotic systems. The denoising scheme has been constructed to be appropriate for short and transient time sequences using circular state space embedding. Its effectiveness was successfully tested on simulated signals as well as on ERPs recorded from within a human brain. The method enables the study of individual ERPs against strong ongoing brain electrical activity.
Study of Denoising in TEOAE Signals Using an Appropriate Mother Wavelet Function  [PDF]
Habib Alizadeh Dizaji,Mohammad Djavad Abolhasani,Alireza Ahmadian,Yousef Salimpour
Audiology , 2007,
Abstract: Background and Aim: Matching a mother wavelet to class of signals can be of interest in signal analy-sis 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 pro-vide a quantitative approach to the problem of matching a mother wavelet to TEOAE signals by us-ing tun-ing curves and to use it for analysis and denoising TEOAE signals. Approximated mother wave-let for TEOAE signals was calculated using an algorithm for designing wavelet to match a specified sig-nal.Materials and Methods: In this paper a tuning curve has used as a template for designing a mother wave-let 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 calcu-lated 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 analyz-ing, 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 ra-tio up to dB.Conclusion: The wavelet generated from this algorithm was remarkably similar to the biorthogonal wave-lets. Therefore, by matching a biorthogonal wavelet to the tuning curve and using wavelet packet analy-sis, a high resolution time-frequency analysis for the otoacoustic emission signals is possible.
Denoising of Biological Signals Using Different Wavelet Based Methods and Their Comparison
V.V.K.D.V. Prasad,P. Siddaiah,B. Prabhakara Rao
Asian Journal of Information Technology , 2012,
Abstract: Denoising of EEG signals using different wavelet shrinkage methods is proposed in this study. We applied these methods to denoise EEG signal contaminated with additive Gaussian noise. In these methods Visu Shrink, minimizing the False Discovery Rate (minFDR), Top, Hypothesis Testing thresholding rules and Hard, Soft thresholding filters are considered. The performances of these methods are evaluated and the results are compared using Mean Square Error (MSE) and Signal to Noise Ratio (SNR). Experiments revealed that minFDR and Hypothesis Testing rules with Hard thresholding filter and Top rule with Soft thresholding filter perform superior to other combinations of thresholding rules and filters.
Adaptive Bayesian Denoising for General Gaussian Distributed (GGD) Signals in Wavelet Domain  [PDF]
Masoud Hashemi,Soosan Beheshti
Statistics , 2012,
Abstract: Optimum Bayes estimator for General Gaussian Distributed (GGD) data in wavelet is provided. The GGD distribution describes a wide class of signals including natural images. A wavelet thresholding method for image denoising is proposed. Interestingly, we show that the Bayes estimator for this class of signals is well estimated by a thresholding approach. This result analytically confirms the importance of thresholding for noisy GGD signals. We provide the optimum soft thresholding value that mimics the behavior of the Bayes estimator and minimizes the resulting error. The value of the threshold in BayesShrink, which is one of the most used and efficient soft thresholding methods, has been provided heuristically in the literature. Our proposed method, denoted by Rigorous BayesShrink (R-BayesShrink), explains the theory of BayesShrink threshold and proves its optimality for a subclass of GDD signals. R-BayesShrink improves and generalizes the existing BayesShrink for the class of GGD signals. While the BayesShrink threshold is independent from the wavelet coefficient distribution and is just a function of noise and noiseless signal variance, our method adapts to the distribution of wavelet coefficients of each scale. It is shown that BayesShrink is a special case of our method when shape parameter in GGD is one or signal follows Laplace distribution. Our simulation results confirm the optimality of R-BayesShrink in GGD denoising with regards to Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) index.
DENOISING RESISTIVITY PHOSPHATE "DISTURBANCES" USING HAAR MOTHER WAVELET TRANSFORM (SIDI CHENNANE, MOROCCO)
Saad,Bakkali; Mahacine,Amrani;
Earth Sciences Research Journal , 2008,
Abstract: wavelet transforms originated in geophysics in the early 1980s for the analysis of seismic signals. since then, significant mathematical advances in wavelet theory have enabled a suite of applications in diverse fields. in geophysics, the power of wavelets for analysis of non stationary processes that contain multiscale features, detection of singularities, analysis of transient phenomena, fractal and multifractal processes, and signal compression is now being exploited for the study of several processes including resistivity surveys. the present paper deals with denoising moroccan phosphate "disturbances" resistivity data? map using the haar wavelet mother transform method. the results show a significant suppression of noise and a very good smoothing and recovery of resistivity anomalies.
DENOISING RESISTIVITY PHOSPHATE “DISTURBANCES” USING HAAR MOTHER WAVELET TRANSFORM (SIDI CHENNANE, MOROCCO)  [cached]
Bakkali Saad,Amrani Mahacine
Earth Sciences Research Journal , 2008,
Abstract: Wavelet transforms originated in geophysics in the early 1980s for the analysis of seismic signals. Since then, significant mathematical advances in wavelet theory have enabled a suite of applications in diverse fields. In geophysics, the power of wavelets for analysis of non stationary processes that contain multiscale features, detection of singularities, analysis of transient phenomena, fractal and multifractal processes, and signal compression is now being exploited for the study of several processes including resistivity surveys. The present paper deals with denoising Moroccan phosphate "disturbances" resistivity data? map using the Haar wavelet mother transform method. The results show a significant suppression of noise and a very good smoothing and recovery of resistivity anomalies.
Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage  [PDF]
Yan-long Chen,Pei-lin Zhang,Bing Li,Ding-hai Wu
Shock and Vibration , 2014, DOI: 10.1155/2014/848097
Abstract: 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. 1. Introduction Safety of a mechanical system is very important for industry. The study of fault feature detection in machinery has thus received considerable attentions during the past decades. Among all detection methods, the most popular tool is vibration-based analysis. However, for the studies about practical mechanical vibration signals, noise is an inevitable factor in the measured signals which always inhibits the extraction of true signal signatures for diagnosis. Therefore, noise depressing in mechanical time series is an important issue for accurate fault diagnosis. Compared with conventional methods, as an effective analysis technique, wavelet transform is a frequently used tool for nonstationary signal processing in many fields. Various shrinkage strategies in the wavelet domain have been proposed for denoising. The most popular methods are VisuShrink, SureShrink, BayesShrink, and NeighShrink. Recently, several shrinkage functions have been modified for better noise reduction based on the above shrinkage approaches [1–8]. Chesneau et al. [1] presented a stein block thresholding algorithm for denoising -dimensional data. Taylor et al. [2] denoised single-molecule fluorescence resonance energy trajectories using wavelet detail thresholding. The paper [3] described a new shrinkage methodology based on non-Gaussian statistical modelling for multimodal image denoising. Liu et al. [4] explored a suitable threshold in a complete solution space using particle swarm optimization. The work [5] developed a
Application of Wavelet Transform in MCG-signal Denoising  [cached]
Yucai Dong,Hongtao Shi,Junzhi Luo,Gehua Fan
Modern Applied Science , 2010, DOI: 10.5539/mas.v4n6p20
Abstract: In this paper, the principle of denoising with wavelet transform are discussed. The application of wavelet threshold denoising method in MCG-signal processing problem is introduced. Program is written by MATLAB to realize wavelet threshold denoising method. The results show that the wavelet threshold denoising method in the MCG-signal denoising can not only restrain the noise effectively, but also reserve the fault character information in the original signal. The signals by this method are better improvements than traditional method.
Image Denoising Based on Wavelet Transform
基于小波变换的图像去噪

XIONG Jiang,
熊江

计算机科学 , 2007,
Abstract: Compareed with traditional Fourier-transform denoising, wavelet denoising can make the image denoising and keep the detail of the image. This paper is mainly about choosing of the thresholding function, ascertaining of the threshold and wavelet denosing.
PERFORMANCE ANALYSIS OF WAVELET THRESHOLDING METHODS IN DENOISING OF AUDIO SIGNALS OF SOME INDIAN MUSICAL INSTRUMENTS
NEEMA VERMA,A. K. VERMA
International Journal of Engineering Science and Technology , 2012,
Abstract: It is known that signals obtained from the real world environment are corrupted by the noise. This noise causes poor performance of the relevant system and therefore must be removed effectively before further processing of signal. Research in the area of wavelets showed that wavelet shrinkage method performs well and efficiently as compared to other methods of denoising. In this paper, a comparative analysis of the performance of various wavelet coefficients hresholding methods presented. For thresholding of the wavelet coefficients, performance of some well-known thresholding methods i.e. Minimax, SURE (Heuristic and Rigorous) and Square-Root-Log are investigated in the presence of white Gaussian noise. The effect of wavelet decomposition levels is also investigated. For wavelet decomposition, Coif5 wavelet is used. The quality of denoised speech signal is expressed in terms of Peak Signal to Noise Ratio (PSNR) as compared to original noiseless speech signal.
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