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Performance Analysis of Image Denoising Technique using Neural Network

by Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 13
Year of Publication: 2015
Authors: Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena
10.5120/21764-5013

Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena . Performance Analysis of Image Denoising Technique using Neural Network. International Journal of Computer Applications. 122, 13 ( July 2015), 36-39. DOI=10.5120/21764-5013

@article{ 10.5120/21764-5013,
author = { Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena },
title = { Performance Analysis of Image Denoising Technique using Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 13 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number13/21764-5013/ },
doi = { 10.5120/21764-5013 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:29.832450+05:30
%A Shrish Pathak
%A Mukesh Kumar
%A A.k.jaiswal
%A Rohini Saxena
%T Performance Analysis of Image Denoising Technique using Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 13
%P 36-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing is widely applied in various area of applications such as Medical, military, agriculture, etc. . The problem which generally occurs in image processing is the removal of noise generated due to various sources. In this paper a new approach based on neural network technique is proposed for the removal of noise. This technique follows three levels. This technique combines the advantages of filtering, neural network and bayes shrinkage technique. The noisy image is first passed through a bilateral filter and neural network is applied to the filtered image and the output of NN is then applied to bayes shrink. The proposed method outperforms other methods both visually and in case of objective quality peak-signal-to-noise ratio (PSNR) and MSE. Proposed method is verified for additive white Gaussian noise.

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Index Terms

Computer Science
Information Sciences

Keywords

Image denoising wavelet threshoding Bayes threshold Multiscale product threshold PSNR MSE DWT IDWT

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