A Comparative Study on Thresholding Methods in Wavelet-based Image Denoising

Xiao, Fei; Zhang, Yungang

doi:10.1016/j.proeng.2011.08.749

Cited by 44 publications

(18 citation statements)

References 9 publications

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“…In hard thresholding coefficients less than threshold T are set to zero while other coefficients are remained unchanged as represented by (1). It has been observed that soft thresholding outperforms hard thresholding but there are some cases where hard thresholding yields a much superior result [10]. All threshold functions can further be enhanced as adaptive method to improve the result based on application, one such approaches are given in [7,9].…”

Section: Thresholding Functionmentioning

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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Section: Thresholding Functionmentioning

confidence: 99%

A Comparative Study of Wavelet Thresholding for Image Denoising

Dixit¹,

Sharma²

2014

IJIGSP

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“…e deficiency of hard thresholding is its discontinuity, while the drawback of soft thresholding is that it causes constant deviation [9]. Fei Xiao and Yungang Zhang explored the properties of several representative thresholding techniques in wavelets denoising in 2011 [10]. Zhang et al proposed an improved threshold function to overcome the drawbacks of hard thresholding and soft thresholding functions in 2019 [9].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

Zeng

Zhang

Zhao

et al. 2020

Computational and Mathematical Methods in Medicine

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Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

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“…The effectiveness of thresholding for denoising image depends on the selection of a suitable threshold such as VisuShrink, BayesShrink, SureShrink, etc. [8].…”

Section: Introductionmentioning

confidence: 99%

Digital Image Denoising Techniques in Wavelet Domain with another Filter: A review

Ferzo

Mustafa

2020

ACAD J NAWROZ UNIV

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Image denoising is a challenging issue found in diverse image processing and computer vision problems. There are various existing methods investigated to denoising image. The essential characteristic of a successful model that denoising image is that it should eliminate noise as far as possible and edges preserving and necessary image information by improving visual quality. This paper presents a review of some significant work in the field of image denoising based on that the denoising methods can be roughly classified as spatial domain methods, transform domain methods, or can mix both to get the advantages of them. This work tried to focus on this mixing between using wavelet transform and the filters in spatial domain to show spatial domain. There have been numerous published algorithms, and each approach has its assumptions, advantages, and limitations depending on the various merits and noise. An analyzing study has been performed comparative in their methods to achieve the denoising algorithms, filtering approach and wavelet-based approach. Standard measurement parameters have been used to compute results in some studies to evaluate techniques while other methods applied new measurement parameters to evaluate the denoising techniques.

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A Comparative Study on Thresholding Methods in Wavelet-based Image Denoising

Cited by 44 publications

References 9 publications

A Comparative Study of Wavelet Thresholding for Image Denoising

A Comparative Study of Wavelet Thresholding for Image Denoising

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

Digital Image Denoising Techniques in Wavelet Domain with another Filter: A review

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