We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Image Denoising using Neighbors Variation with Wavelet

by S D Ruikar, D D Doye
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 17
Year of Publication: 2012
Authors: S D Ruikar, D D Doye
10.5120/5783-8085

S D Ruikar, D D Doye . Image Denoising using Neighbors Variation with Wavelet. International Journal of Computer Applications. 42, 17 ( March 2012), 8-15. DOI=10.5120/5783-8085

@article{ 10.5120/5783-8085,
author = { S D Ruikar, D D Doye },
title = { Image Denoising using Neighbors Variation with Wavelet },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 17 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number17/5783-8085/ },
doi = { 10.5120/5783-8085 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:32.209934+05:30
%A S D Ruikar
%A D D Doye
%T Image Denoising using Neighbors Variation with Wavelet
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 17
%P 8-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The image gets corrupted by Additive White Gaussian Noise during the process of acquisition, transmission, storage and retrieval. Denoising refers to suppressing the noise while retaining the edges and other important detailed structures as much as possible. This paper presents a general structure of the recovery of images using a combination of variation methods and wavelet analysis. The variation formulation of the problem allows us to build the properties of the recovered signal directly into the analytical machinery. The efficient wavelet representation allows us to capture and preserve sharp features in the signal while it evolves in accordance with the variation laws. We propose the three different variation model for removing noise as Horizontal, vertical and Cluster. Horizontal and Vertical variation model obtained the threshold at each decomposed level of Wavelet. Cluster variation model moving mask in different wavelet sub band. This proposed scheme has better PSNR as compared to other existing technique.

References
  1. Shan GAI, Peng LIU, Jiafeng LIU, Xianglong TANG, "A New Image Denoising Algorithm via Bivariate Shrinkage Based on Quaternion Wavelet Transform", Journal of Computational Information Systems November 2010,3751-3760.
  2. Murat Belge, Misha E. Kilmer, and Eric L. Miller, Member, IEEE, "Wavelet Domain Image Restoration with Adaptive Edge-Preserving Regularization", IEEE Transactions On Image Processing, VOL. 9, NO. 4, APRIL 2000 597-608.
  3. S. Annadurai, R. Shanmugalakshmi, "Fundamentals of Digital Image Processing", Pearson Education, 2008.
  4. David L. Donoho. "De-noising by soft-thresholding. " IEEE Trans. on Information Theory, Vol 41, No. 3, May 1995.
  5. D. L. Donoho and I. M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. The Journal of Amer. Statist. Assoc. 1995, 90(432):1200-1224.
  6. F. Luisier, T. Blu, and M. Unser. A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding. IEEE Trans. Image Processing. 2007, 16(3):593-606.
  7. L. Rudin, S. Osher, and E. Fatem, "Nonlinear Total Variation Based Noise Removal Algorithms", Physical D, 60:259-268, 1992.
  8. A. Haddad, Y. Meyer, "An Improvement of Rudin Osher Fatemi Model", Applied and Computational Harmonic Analysis. 22 (2007) 319–334.
  9. Stanley Osher, Andres Sole, and Luminita Vese, "Image Decomposition and Restoration Using Total Variation Minimization and the H?1 Norm", Multiscale Model. Simulation, 2003 Society for Industrial and Applied Mathematics, Vol. 1, No. 3, Pp. 349–370.
  10. Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, Univ. Lecture Ser. 22, Ams, Providence, RI, 2002.
  11. Fang fang Dong, Jeanine Yang, Chunxiao Liu, And De-Xing Kong, "A Fast Algorithm For Vectorial TV-Based Image Restoration", SIAM International Journal Of Numerical Analysis And Modeling
  12. Yang Wang and Haomin Zhou, "Total Variation Wavelet-Based Medical Image Denoising", International Journal of Biomedical Imaging Volume 2006, Article ID 89095, Pages 1–6
  13. 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 pp 57-74.
  14. Gabriele Steidl, Joachim Weickert, Thomas Brox, Pavel Mrazek, and Martin Welk, "On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and Sides", SIAM Journal of Numerical. Analysis. , 2004 Society For Industrial And Applied Mathematics Vol. 42, No. 2, Pp. 686–713
  15. Antonin Chambolle, "An Algorithm for Total Variation Minimization and Applications", Journal of Mathematical Imaging and Vision 20: 89–97, 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
  16. Paul Rodriguez, Brendt Wohlberg, "A Generalized Vector-Valued Total Variation Algorithm", 2009 IEEE, ICIP 2009, Pp1309-1312.
  17. Yilun Wang, Junfeng Yang, Wotao Yin, and Yin Zhang, "A New Alternating Minimization Algorithm for Total Variation Image Reconstruction", Rice University Technical Report TR 07-10.
  18. Banazier A. Abrahim, Yasser Kadah, "Speckle Noise Reduction Method Combining Total Variation and Wavelet Shrinkage for Clinical Ultrasound Imaging", IEEE 2011.
  19. David C. Dobsony and Curtis R. Vogel, "Convergence of an Iterative Method for Total Variation Denoising", SIAM Journal of Numerical. Anal. C 1997 Society For Industrial And Applied Mathematics Vol. 34, No. 5, Pp. 1779-1791, October 1997
  20. Rick Chartrand, "Numerical Differentiation of Noisy Non smooth Data", International Scholarly Research Network ISRN Applied Mathematics Volume 2011, Article ID 164564, 11 pages
  21. C Sidney Burrus, Ramesh A Gopinath, and Haitao Guo, "Introduction to wavelet and wavelet transforms", Prentice Hall1997.
  22. S. Mallat, A Wavelet Tour of Signal Processing, Academic, New York, second edition, 1999.
  23. R. C. Gonzalez and R. Elwood's, Digital Image Processing. Reading, MA: Addison-Wesley, 1993.
  24. M. Sonka, V. Hlavac, R. Boyle Image Processing, Analysis, And Machine Vision. Pp10-210 & 646-670
  25. Raghuveer M. Rao, A. S. Bopardikar Wavelet Transforms: Introduction to Theory and Application Published by Addison-Wesley 2001 pp1-126.
  26. Arthur Jr Weeks, Fundamental of Electronic Image Processing PHI 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Horizontal Variation Vertical Variation Cluster Variation Wavelet Noise