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Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising

by Reena Singh, V. K. Srivastava
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 14
Year of Publication: 2013
Authors: Reena Singh, V. K. Srivastava
10.5120/13588-1318

Reena Singh, V. K. Srivastava . Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising. International Journal of Computer Applications. 78, 14 ( September 2013), 1-6. DOI=10.5120/13588-1318

@article{ 10.5120/13588-1318,
author = { Reena Singh, V. K. Srivastava },
title = { Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number14/13588-1318/ },
doi = { 10.5120/13588-1318 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:32.348012+05:30
%A Reena Singh
%A V. K. Srivastava
%T Weighted Non-Linear Diffusion Filtering with Wavelet Thresholding in Image Denoising
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 14
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wavelet based image denoising is an important technique in the area of image noise reduction. In this paper, a new adaptive wavelet based image denoising algorithm in the presence of Gaussian noise is developed. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate wavelet coefficients unchanged. Since noise affects both approximate as well as detail coefficients, the proposed technique incorporates methods to eliminate noise in both types of coefficients. The propose technique is applied in two phases. In the first phase, an adaptive data driven threshold for image denoising via wavelet soft-thresholding is applied on detail coefficients. In the second phase of the proposed algorithm, anisotropic diffusion is applied on approximate coefficients. In this context, a weighted diffusivity function is proposed which incorporates contextual discontinuities in the image. The diffusivity function derived is applied depending on local image features and hence improve the capability of feature preservation along with noise removal. The proposed technique was applied on standard noisy image and the results obtained show the superiority of the method over other wavelet based denoising techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

Wavelet thresholding BayesShrink anisotropic diffusion weighted diffusivity function denoising wavelet coefficients.

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