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An Improved Adaptive Wavelet Thresholding Image Denoising Method

Published on February 2015 by R Vijaya Arjunan, B Kishore, N Sivaselvan
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Advanced Computing and Communication Techniques for High Performance Applications
Foundation of Computer Science USA
ICACCTHPA2014 - Number 2
February 2015
Authors: R Vijaya Arjunan, B Kishore, N Sivaselvan
d9bfbd91-6853-4548-a7a3-c93b07243f7e

R Vijaya Arjunan, B Kishore, N Sivaselvan . An Improved Adaptive Wavelet Thresholding Image Denoising Method. Advanced Computing and Communication Techniques for High Performance Applications. ICACCTHPA2014, 2 (February 2015), 23-27.

@article{
author = { R Vijaya Arjunan, B Kishore, N Sivaselvan },
title = { An Improved Adaptive Wavelet Thresholding Image Denoising Method },
journal = { Advanced Computing and Communication Techniques for High Performance Applications },
issue_date = { February 2015 },
volume = { ICACCTHPA2014 },
number = { 2 },
month = { February },
year = { 2015 },
issn = 0975-8887,
pages = { 23-27 },
numpages = 5,
url = { /proceedings/icaccthpa2014/number2/19441-6022/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Advanced Computing and Communication Techniques for High Performance Applications
%A R Vijaya Arjunan
%A B Kishore
%A N Sivaselvan
%T An Improved Adaptive Wavelet Thresholding Image Denoising Method
%J Advanced Computing and Communication Techniques for High Performance Applications
%@ 0975-8887
%V ICACCTHPA2014
%N 2
%P 23-27
%D 2015
%I International Journal of Computer Applications
Abstract

The NeighShrink, IAWDMBNC, and IIDMWT are some familiar methods for noise minimization from corrupted image. However, this mentioned method suffers from optimal recovery of the original image since the threshold value does not minimize the noisy wavelet coefficients across the image scale factor. In this paper, we propose an improved denoising method that provides an adaptive way of setting up minimum threshold by shrinking the wavelet coefficients so as to overcome the above problem using a new modified exponential function. The experimental analysis qualifying image such as Peak to Signal Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are found better than the NeighShrink, IAWDMBNC, and IIDMWT methods. Moreover, our method retains the original image information with high visual quality.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Image Noise Wavelet Transform Thresholding Psnr Ssim