CFP last date
20 January 2025
Reseach Article

Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function

by Sabahaldin A. Hussain, Sami M. Gorashi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 13
Year of Publication: 2012
Authors: Sabahaldin A. Hussain, Sami M. Gorashi
10.5120/5751-7960

Sabahaldin A. Hussain, Sami M. Gorashi . Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function. International Journal of Computer Applications. 42, 13 ( March 2012), 5-13. DOI=10.5120/5751-7960

@article{ 10.5120/5751-7960,
author = { Sabahaldin A. Hussain, Sami M. Gorashi },
title = { Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 13 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 5-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number13/5751-7960/ },
doi = { 10.5120/5751-7960 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:12.235153+05:30
%A Sabahaldin A. Hussain
%A Sami M. Gorashi
%T Image Denoising based on Spatial/Wavelet Filter using Hybrid Thresholding Function
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 13
%P 5-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a hybrid denoising algorithm which combines spatial domain bilateral filter and hybrid thresholding function in the wavelet domain is proposed. The wavelet transform is used to decompose the noisy image into its different subbands namely LL, LH, HL, and HH. A two stage spatial bilateral filter is applied. The first stage is applied on the noisy image before wavelet decomposition. This stage will be called a pre-processing stage. The second stage spatial bilateral filtering is applied on the low frequency subband of the decomposed noisy image namely subband LL. This stage will tend to cancel or at least attenuate any residual low frequency noise components. The intermediate stage deal with high frequency noise components by thresholding detail subbands LH, HL, and HH using hybrid thresholding function. The experimental results show that the performance of the proposed denoising algorithm is superior to that of the conventional denoising approach.

References
  1. H. Guo, J. E. Odegard, M. Lang, R. A. Gopinath, I. W. Selesnick, and C. S. Burrus, "Wavelet based speckle reduction with application to SAR based ATD/R", First International Conference on Image Processing, pp. 75-79, 1994.
  2. R. D. Nowak, "Wavelet based Rician noise removal", IEEE Trans. Image Processing, Vol. 8, No. 10, pp. 1408-1419, 1999.
  3. ) S. G. Chang, B. Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression" IEEE Trans. Image Processing, Vol. 9, No. 9, pp. 1532-1546, 2000.
  4. S. G. Chang, B. Yu and M. Vetterli, "Spatially adaptive wavelet thresholding with context modeling for image denoising," IEEE Trans. Image Processing, Vo1. 9, No. 9, pp. 1522-1531, 2000.
  5. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain," IEEE Trans. Image Processing, Vol. 12, No. 11, pp. 1338-1351, 2003.
  6. F. Luisier, T. Blu, M. Unser, " A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding", IEEE Trans. Image Processing, Vol. 16, No. 3, pp. 593-606, 2007.
  7. Z. Dengwen, C. Wengang,"Image denoising with an optimal threshold and neighboring window", Pattern Recognition Letters, 29, pp. 1694-1697, 2008.
  8. S. M. Smith and J. M. Brady, "Susan - A new approach to low level image processing", Int. Journal of Computer Vision, Vol. 23, pp. 45–78, 1997.
  9. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images", Proc. Int. Conf. Computer Vision, 1998, pp. 839–846.
  10. R. Kimmel, N. Sochen and A. M. Bruckstein, "Diffusions and on fusions in signal and image processing", Mathematical Imaging and Vision, Vol. 14, No. 3, pp. 195–209, 2001.
  11. R. Kimmel, A. Spira and N. Sochen, "A short time Beltrami kernel for smoothing images and manifolds", IEEE Trans. Image Processing, Vol. 16, No. 6, pp. 1628–1636, 2007.
  12. H. Phelippeau, H. Talbot, M. Akil, S. Bara,; , "Shot noise adaptive bilateral filter", 9th International Conference on Signal Processing, pp. 864-867, 2008
  13. B. K Gunturk, "Fast bilateral filter with arbitrary range and domain kernels", IEEE Trans. Image Processing, Vol. 20, No. 9, pp. 2690-2696, 2011.
  14. L. Sun, O. C. Au, R. Zou, W. Dai, X. Wen, S. Lin, J. Li, "Adaptive bilateral filter considering local characteristics," Sixth International Conference on Image and Graphics (ICIG), pp. 187-192, 2011.
  15. D. L. Donoho and I. M. Johnstone, "Ideal spatial adaptation via wavelet shrinkage", Biometrika, 81, pp. 425-455, 1994.
  16. D. L. Donoho, "Denoising by soft thresholding", IEEE Trans. on IT, pp. 613–627, 1995.
  17. D. L. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Journal of American Statistical Association, 90(432):1200-1224, December 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Image Denoising Spatial Bilateral Filter Thresholding Function