Research Article
Two Stage Wavelet based Image Denoising
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 56 - Issue 14 |
| Published: October 2012 |
| Authors: Glincy Abraham, Neethu Mohan, Sreekala S, Neethu Prasannan, K P Soman |
10.5120/8961-3167
|
Glincy Abraham, Neethu Mohan, Sreekala S, Neethu Prasannan, K P Soman . Two Stage Wavelet based Image Denoising. International Journal of Computer Applications. 56, 14 (October 2012), 30-37. DOI=10.5120/8961-3167
@article{ 10.5120/8961-3167,
author = { Glincy Abraham,Neethu Mohan,Sreekala S,Neethu Prasannan,K P Soman },
title = { Two Stage Wavelet based Image Denoising },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 56 },
number = { 14 },
pages = { 30-37 },
doi = { 10.5120/8961-3167 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Glincy Abraham
%A Neethu Mohan
%A Sreekala S
%A Neethu Prasannan
%A K P Soman
%T Two Stage Wavelet based Image Denoising%T
%J International Journal of Computer Applications
%V 56
%N 14
%P 30-37
%R 10.5120/8961-3167
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper we are proposing a 2-stage wavelet based denoising technique. First stage of denoising is performed on the approximation coefficient obtained from the level 1 wavelet decomposition [1] of the noisy image and second stage of denoising is applied on the reconstructed image. The second stage denoising has shown a better result on to the reconstructed image. The detail coefficients are newly estimated from the first level denoised approximation coefficients. For denoising, techniques like Total Variation [3], Split Bregman [4] and NL means [5] are used. The quality of results obtained from different denoising techniques has been measured using various objective matrices such as PSNR, MSE on standard test images.
References
- Stephane G Mallat "A theory for multiresolution signal decomposition:The wavelet representation"IEEE Transactions On Pattern Analysis And Machine Intelligence,Vol 11, No. 7. July 1989
- Nilamani Bhoi, Dr. Sukadev Meher" Total Variation based Wavelet Domain Filter for Image Denoising" First International Conference on Emerging Trends in Engineering and Technology
- L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. PhysicaD, 60:259–268, 1992
- Jacqueline Bush "Bregman Algorithms" Senior Thesis. University of California, Santa Barbara, June 10, 2011
- A. Buades, B. Coll, and J Morel "A non-local algorithm for image denoising", IEEE International Conference on Computer vision and Pattern Recognition,2005
- John Canny, member, IEEE, "A Computational Approach to Edge Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. PAMI-8, NO. 6, November 1986
- Bregman Algorithms Author: JacquelineBush Supervisor Dr. CarlosGarc´?a-Cervera June 10, 2011
- R C. Gonzalez and R. E Woods,Digital Image Processing, Addison Wesley Longman Inc. ,2000.
- Kossi Edoh and John Paul Roop "A Fast Wavelet Multilevel Approach to Total Variation Image Denoising", International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No. 3,September 2009.
- Dr. K. P. Soman, R. Ramanathan "Chapter 25, Digital Signal and Image Processing- The Sparse Way" 2012 by Isa Publishers.
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