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An Improved Method of Removing Gaussian Noise for a Gray Scale Image using Multiresolution Technique

by Sakshi Thakral, Mandeep Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 21
Year of Publication: 2013
Authors: Sakshi Thakral, Mandeep Singh
10.5120/10592-5706

Sakshi Thakral, Mandeep Singh . An Improved Method of Removing Gaussian Noise for a Gray Scale Image using Multiresolution Technique. International Journal of Computer Applications. 63, 21 ( February 2013), 43-46. DOI=10.5120/10592-5706

@article{ 10.5120/10592-5706,
author = { Sakshi Thakral, Mandeep Singh },
title = { An Improved Method of Removing Gaussian Noise for a Gray Scale Image using Multiresolution Technique },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 21 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number21/10592-5706/ },
doi = { 10.5120/10592-5706 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:59.884400+05:30
%A Sakshi Thakral
%A Mandeep Singh
%T An Improved Method of Removing Gaussian Noise for a Gray Scale Image using Multiresolution Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 21
%P 43-46
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a new image denoising algorithm using wavelets. It utilizes the pertinence of the neighbor wavelet coefficients by using the block thresholding scheme. Proposed enjoys a number of advantages over the other conventional image denoising methods. The aim of this paper is to investigate a multiresolution technique and the corresponding thresholding methods for image denoising. Consideration may also be given to applying some enhancement techniques to the existing methods so as to achieve both noise reduction and feature preservation. The noise acceptance and rejections rates have been computed for the existing techniques and the newly developed technique. The proposed technique provides better results with the soft thresholding and block thresholding based on parameters, MSE and PSNR

References
  1. Donoho, D. L. and Johnstone I. M. 1994. Ideal spatial adaptation by wavelet shrinkage, Biometrika,vol. 81, no. 3, pp. 425–455.
  2. Zhou, D. and Xiaoliu,S. 2008. Image Denoising Using Block Thresholding ,congress on image and signal processing
  3. Donoho D. L. 1995. Denoising by soft-thresholding, IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613–627.
  4. Chang,S. G. ,Yu,B. and Vetterli,M. 2000. Adaptive wavelet thresholding for image denoising and compression, IEEE. Image Process. 9(9), pp. 1532–1546.
  5. Donoho,D. L 1993. De-Noising by Soft Thresholding, IEEE Trans. Info. Theory 43, pp. 933-936.
  6. Guo, L. and Jiaxue, L. 2009. Image Denoising using Adaptive Threshold based on Second Generation Wavelets Transform, IFCSTA, pp. 444-447.
Index Terms

Computer Science
Information Sciences

Keywords

Image Denoising Wavelet Transform Soft Thresholding Block Thresholding Noise Variance

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