Image Denoising Using Block Thresholding

Zhou, Dengwen; Xiaoliu, Shen

doi:10.1109/cisp.2008.131

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…These techniques are popularly called as thresholding or wavelet shrinkage. The multiscale noise reduction techniques such as the BayesShrink [22], multiscale product thresholding (MPT) [24], ProbShrink [27], Stein's unbiased risk estimation with linear expansion of threshold (SURELET) [25], interscale orthonormal wavelet thresholding (IOWT) [23], block thresholding (BT) [47], and NeighShrinkSURE (NSS) [26] work in logarithmic domain. The GLM-based wavelet filter proposed by Pizurica et al [28] is implemented using the multiplicative noise model.…”

Section: Multiscale Techniquesmentioning

confidence: 99%

“…The log operation decouples the multiplicative components into independent individual noise-free and noise components [2,5,6,20]. Filters such as DPAD [17], generalized likelihood estimation method (GLM) [28], NLM [39], PPB [40], and anisotropic TV (ATV) [19] are implemented using the multiplicative noise model and all other filters [6][7][8][9][20][21][22][23][24][25][26]34,41,42,46,47] are implemented using log transformation of the input image.…”

Section: Despeckling Techniquesmentioning

confidence: 99%

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Speckle Noise Reduction in B-Mode Echocardiographic Images: A comparison

Biradar

Dewal

Rohit

2015

IETE Technical Review

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This paper presents the despeckling applications of 48 filters for B-mode echocardiographic images. The filters are grouped into eight types, namely, local statistics, fuzzy, Fourier, multiscale, nonlinear iterative, total variation, nonlocal mean, and hybrid. The thrust areas of analyses are noise suppression, edge, and structure preservation evaluated in terms of image quality metrics, visual quality assessment, and clinical validation. The comparative analysis reveals that filters based on generalized likelihood ratio, local statistics (mean and variance), detail preserving anisotropic diffusion, fast bilateral, sparse representation based beta process factor analysis, and patch-based locally optimal Wiener and probabilistic nonlocal means stand out among the techniques being considered for comparison.

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Section: Multiscale Techniquesmentioning

confidence: 99%

Section: Despeckling Techniquesmentioning

confidence: 99%

Speckle Noise Reduction in B-Mode Echocardiographic Images: A comparison

Biradar

Dewal

Rohit

2015

IETE Technical Review

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“…Moreover, different types of dependencies between wavelet coefficients also are important factors of the threshold selection. Such as, interscale dependency [6] which takes into account the dependency between scales, and intrascale dependency [7] which considers the dependency between the coefficients in each subband. The NeighLevel method was proposed in [8] by cons idering the interscale and intrascale dependencies of the coefficients.…”

Section: Introductionmentioning

confidence: 99%

An efficient method for the unmanned aerial vehicle image denoising

Liu

Deng

2013

2013 IEEE 4th International Conference on Electronics Information and Emergency Communication

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An efficient method for the unmanned aerial ve hicle (UAV) image denoising is proposed by com bining an adaptive wavelet thresholding denoising and th e image enhancement method in this pap er. This method is aimed at improving th e effect of the wavel et thresholding denoising method and en hancing the edge of the image. An adapti ve thres hold value which changes with the change of the decomposition scale and the direction of the subband is selected for denoising. After wavelet denoising, the image enhancement method is used for en hancing the edge of th e image. Exp erimental results show that, comp ared with th e other methods, the proposed method yi elds bigger peak signal-to-noise ratio and superior visual image quality-measured by univers al image quality index. It also retains more edges and deta ils of the image.

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“…Block Shrink is a completely data-driven block thresholding approach and is also easy to implement [13]. It can decide the optimal block size and threshold for every wavelet sub band by minimizing Stein's unbiased risk estimate (SURE).…”

Section: E Block Shrinkmentioning

confidence: 99%

“…It can decide the optimal block size and threshold for every wavelet sub band by minimizing Stein's unbiased risk estimate (SURE). It also limits the block size search range by following [13]- Where, L represents block size, N is an integer with power of two, k represents the scale. The block thresholding simultaneously keeps or kills all the coefficients in groups rather than individually, enjoys a number of advantages over the conventional term-by-term thresholding.…”

Section: E Block Shrinkmentioning

confidence: 99%

A Comparative Study of Wavelet Thresholding for Image Denoising

Dixit¹,

Sharma²

2014

IJIGSP

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Abstract-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.

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Image Denoising Using Block Thresholding

Cited by 13 publications

References 7 publications

Speckle Noise Reduction in B-Mode Echocardiographic Images: A comparison

Speckle Noise Reduction in B-Mode Echocardiographic Images: A comparison

An efficient method for the unmanned aerial vehicle image denoising

A Comparative Study of Wavelet Thresholding for Image Denoising

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