CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Review of Shrinkage Techniques for Image Denoising

by Rajesh Kumar Rai, Jyoti Asnani, T. R. Sontakke
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 19
Year of Publication: 2012
Authors: Rajesh Kumar Rai, Jyoti Asnani, T. R. Sontakke
10.5120/5800-8009

Rajesh Kumar Rai, Jyoti Asnani, T. R. Sontakke . Review of Shrinkage Techniques for Image Denoising. International Journal of Computer Applications. 42, 19 ( March 2012), 13-16. DOI=10.5120/5800-8009

@article{ 10.5120/5800-8009,
author = { Rajesh Kumar Rai, Jyoti Asnani, T. R. Sontakke },
title = { Review of Shrinkage Techniques for Image Denoising },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 19 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number19/5800-8009/ },
doi = { 10.5120/5800-8009 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:43.665943+05:30
%A Rajesh Kumar Rai
%A Jyoti Asnani
%A T. R. Sontakke
%T Review of Shrinkage Techniques for Image Denoising
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 19
%P 13-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image denoising is a common procedure to suppress the quality degradation caused by noise. Several image denoising methods are proposed in literature. Amongst these Discrete Wavelet Transform-DWT Filters are very popular. Denoising using the DWT-Transform includes decomposition of the image into various sub bands and then modeling them as independent identically distributed random variables with Gaussian distribution. Shrinkage methods are often used for suppressing Additive White Gaussian Noise (AWGN), where thresholding is used to retain the larger wavelet coefficients alone. Minimum Mean Square Error estimation is a common practice for noise analysis and is thus included in this paper. Overall we discuss in this review briefly the various Shrinkage methods in DWT-Domain Filters and we assess them by posing a comparison between the efficiency of these filters.

References
  1. D. L. Donoho, I. M. Johnstone, Ideal spatial adaptation via wavelet shrinkage", Biometrika, vol. 81, no. 3, pp. 425- 55, 1994.
  2. D. L. Donoho, I. M. Johnstone, "De-noising by soft- thresholding," IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-27, 1995.
  3. D. L. Donoho, I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage", Journal of the American Statistical Association, vol. 90(432), pp. 1200-1224, 1995
  4. F. Luisier, T. Blu, M. Unser, "A new SURE approach to image denosing: interscale orthonormal wavelet thresholding", IEEE Transactions on Image Processing, vol. 16 (3), pp. 593-606, 2007.
  5. S. Chang, B. Yu, M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression", IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, 2000.
  6. G. Y. Chen, T. D. Bui, A Krzyzak, "Image denoising using neighboring wavelet coefficients", proceedings of IEEE international conference on Acoustics, speech and signal processing '04, pp. 917-920, 2004.
  7. M Mastriani and A. E. Giraldez, "Smoothing of coefficients in wavelet domain for speckle reduction in synthetic aperture radar images", Journal of Graphics Vision and Image Processing, vol. 7(special issue), pp. 1-8, 2007.
  8. A. Pi?zurica and W. Philips, "Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising," IEEE Trans. Image Process. , vol. 15, no. 3, pp. 654–665, Mar. 2006.
  9. L. Sendur and I. W. Selesnick, "Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency," IEEE Trans. Signal Processing, vol. 50, no. 11, pp. 2744–2756, Nov. 2002.
  10. L. Sendur and I. W. Selesnick, "Bivariate shrinkage with local variance estimation," IEEE Signal Process. Lett. vol. 9, no. 12, pp. 438–441, Dec. 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Psnr Shrinkage Methods Thresholding Dwt-domain Filters

Powered by PhDFocusTM