Research Article
An Efficient Implementation of Neighborhood based Wavelet Thresholding For Image Denoising
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 41 - Issue 9 |
| Published: March 2012 |
| Authors: Sabahaldin A. Hussain, Sami M. Gorashi |
10.5120/5566-7653
|
Sabahaldin A. Hussain, Sami M. Gorashi . An Efficient Implementation of Neighborhood based Wavelet Thresholding For Image Denoising. International Journal of Computer Applications. 41, 9 (March 2012), 1-6. DOI=10.5120/5566-7653
@article{ 10.5120/5566-7653,
author = { Sabahaldin A. Hussain,Sami M. Gorashi },
title = { An Efficient Implementation of Neighborhood based Wavelet Thresholding For Image Denoising },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 41 },
number = { 9 },
pages = { 1-6 },
doi = { 10.5120/5566-7653 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Sabahaldin A. Hussain
%A Sami M. Gorashi
%T An Efficient Implementation of Neighborhood based Wavelet Thresholding For Image Denoising%T
%J International Journal of Computer Applications
%V 41
%N 9
%P 1-6
%R 10.5120/5566-7653
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper we propose computationally efficient denoising algorithm that thresholds the wavelet coefficient considering its neighbors in deciding whether it is noisy or noise free. The proposed algorithm select a suboptimal threshold and neighboring window size for every subband that minimized Mean Square Error(MSE) in the denoised image using Stein's Unbiased Risk Estimate(SURE). In this paper, we demonstrate the efficiency of the proposed denoising algorithm as compared with two other state-of-the art denoising algorithms.
References
- D. L. Donoho and Iain M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage", Biometrika, 81, pp. 425-455, 1994.
- D. L. Donoho, Iain M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage", Journal of American Statistical Association, 90(432), pp. 1200-1224, 1995.
- S. G. Chang, B. Yu and M. Vetterli, "Adaptive Wavelet Thresholding for Image Denoising and Compression", IEEE Transactions on Image Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.
- F. Luisier, T. Blu, M. Unser, " A New SURE Approach to Image Denoising:Interscale Orthonormal Wavelet Thresholding", IEEE Transactions on Image Processing, Vol. 16, No. 3, pp. 593-606, 2007.
- R. Song, Y. Li, Q. Zhang, and Z. Zhao," Image denoising method based on non-uniform partition and wavelet transform", International Congress on Image and Signal Processing(CISP), pp. 703–706, 2010.
- Y. Norouzzadeh and M. Rashidi, " Image denoising in wavelet domain using a new thresholding function", International Conference on Information Science and Technology, pp. 721–724, 2011.
- T. T. Cai and B. W. Silverman, " Incorporating information on neighboring coefficients into wavelet estimation", Sankhya A 63 pp. 127–148, 2001.
- G. Y. Chen, T. D. Bui, A. Krzyzak,"Image denoising using neighbouring wavelet coefficients", IEEE Transactions on signal Processing, Vol. 10, No. 10, pp. 917-920, 2004.
- Z. Dengwen, C. Wengang,"Image denoising with an optimal threshold and neighboring window", Pattern Recognition Letters, 29, pp. 1694-1697, 2008.
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