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 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

A Novel Approach for Mixed Noise Removal using ‘ROR’ Statistics Combined WITH ACWMF and DPVM

by Remya Soman, Jency Thomas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 17
Year of Publication: 2014
Authors: Remya Soman, Jency Thomas
10.5120/15076-3442

Remya Soman, Jency Thomas . A Novel Approach for Mixed Noise Removal using ‘ROR’ Statistics Combined WITH ACWMF and DPVM. International Journal of Computer Applications. 86, 17 ( January 2014), 11-17. DOI=10.5120/15076-3442

@article{ 10.5120/15076-3442,
author = { Remya Soman, Jency Thomas },
title = { A Novel Approach for Mixed Noise Removal using ‘ROR’ Statistics Combined WITH ACWMF and DPVM },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 17 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number17/15076-3442/ },
doi = { 10.5120/15076-3442 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:27.014760+05:30
%A Remya Soman
%A Jency Thomas
%T A Novel Approach for Mixed Noise Removal using ‘ROR’ Statistics Combined WITH ACWMF and DPVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 17
%P 11-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a mixed noise removal framework using Robust Outlyingness ratio (ROR) statistics combined with adaptive center weighted median and detail preserving variational approach is discussed. The pixels are classified into different clusters based on the ROR statistics, which measures how impulse like each pixel is . To make the results more accurate, each cluster undergoes coarse and fine stage of noise detection and removal, which make use of ACWMF for noise detection and DPVM for restoration of noise candidates. Final stage of filtering is done by means of Non Local Means filter. Extensive simulations show that the proposed scheme consistently works well in suppressing both impulse and Gaussian noise with different noise ratios.

References
  1. Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing" Prentice-Hall, India, second edition, 2007.
  2. I. Pitas and A. N. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications. Norwell, MA:Kluwer, 1990.
  3. Y. H. Lee and S. A. Kassam, "Generalized median filtering and related nonlinear filtering techniques," IEEE Trans. Acoust. , Speech, Signal Processing, vol. ASSP-33, pp. 672–683, June 1985.
  4. Igor Aizenberg "Effective Impulse Detector Based on Rank-Order Criteria " IEEE Signal Process. Letter. , vol. 11, no. 3, Mar. 2004.
  5. D. Brownrigg, "The weighted median filter," Commun. ACM, vol. 27, no. 8, pp. 807–818, Aug. 1984.
  6. D. Florencio and R. W. Schafer, "Decision-based median filter using local signal statistics," Proc. SPIE, vol. 2308, pp. 268-275, Sept. 1994.
  7. F. Russo and G. Ramponi, "A fuzzy filter for images corrupted by impulse noise," IEEE Signal Process. Lett. , vol. 3, no. 6, pp. 168-170, 1996.
  8. S. J. Ko and Y. H. Lee, "Center weighted media filters and their applications to image enhancement " IEEE Trans. Circuits Syst. , vol. 38, no. 9, pp. 984–993, Sep. 1991.
  9. T. Chen and K. Ma and L. Chen, "Tri-state median filter for image denoising," IEEE Trans. Image Process. , vol. 8, no. 12, pp. 1834-1838, 1999.
  10. T. Sun and Y. Neuvo, "Detail-preserving median based filters in image processing," Pattern Recognit. Lett. , vol. 15, pp. 341-347, Apr. 1994.
  11. Haidi Ibrahim, Nicholas Sia Pik Kong "Simple Adaptive Median Filter for the Removal of Impulse Noise from Highly Corrupted Images", IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, NOVEMBER 2008.
  12. V. Crnojevic, V. Senk, and Z. Trpovski, "Advanced impulse detection based on pixel-wise MAD," IEEE Signal Process. Lett. , vol. 11, no. 7,pp. 589–592, Jul. 2004.
  13. C. -T. Chen and L. -G. Chen, "A self-adjusting weighted median filter for removing impulse noise in image," in Proc. IEEE Int. Conf. on Image Processing, 1998, pp. 419–422.
  14. D. A. F. Florencio and R. W. Schafer, "Decision-based median filter using local signal statistics," Proc. SPIE, vol. 2308, pp. 268–275, 1994.
  15. T. Kasparis, N. S. Tzannes, and Q. Chen, "Detail-preserving adaptive conditional median filters," J. Electron. Imag. , vol. 1, no. 14, pp. 358–364, 1992.
  16. Y. Dong an S. Xu "A new directional weighted median filter for removal of random-value impulse noise " IEEE Signal Process. Letter. , vol. 14, no. 3, pp. 193–196, Mar. 2007.
  17. Pok, J. C. Liu, and A. S. Nair, "Selective removal of impulse noise based on homogeneity level information," IEEE Trans. Image Process. , vol. 12, no. 1, pp. 85–92, Jan. 2003.
  18. R. Garnett, T. Huegerich, C. Chui, and W. J. He, "A universal noise removal algorithm with an impulse detector," IEEE Trans. Image Process. , vol. 14, no. 11, pp. 1747–1754, Nov. 2005.
  19. Y. Dong, R. H. Chan, and S. Xu, "A detection statistic for random valued impulse noise," IEEE Trans. Image Process. , vol. 16, no. 4, pp. 1112–1120, Mar. 2007.
  20. Raymond H. Chan , Chen Hu and Mila Nikolova, "An iterative Procedure for removing random-valued impulse noise. ," IEEE Signal Process. Letter. , vol. 11, no. 12, pp. 921–924, Dec. 2004.
  21. M. Nikolova, "A variational approach to remove outliers and impulsenoise," J. Math. Imag. Vis. , vol. 20, pp. 99–120, 2004.
  22. M. Nikolova, "Minimizers of cost-functions involving nonsmooth data-fidelity terms. Application to the processing of outliers," SIAM J. Numer. Anal. , vol. 40, pp. 965–994, 2002.
  23. K. Aiswarya, V. Jayaraj, and D. Ebenezer, "A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos," in Second Int. Conf. Computer Modeling and Simulation,2010, pp. 409–413.
  24. S. Esakkirajan, T. Veerakumar, Adabala N. Subramanyam, and C. H. PremChand "Removal of High Density Salt and Pepper Noise Through Modified Decision Based unsymmetricTrimmed Median Filter "IEEE Signal Process. Letter. , vol. 18, no. 5, May. 2011.
  25. W. Luo, "A new efficient impulse detection algorithm for the removal of impulse noise," IEICE Trans. Fundam. Electron. , Commun. , Comput. , vol. E88-A, no. 10, pp.
  26. Kenny Kal Vin Toh and Nor Ashidi Mat Isa "Cluster-Based Adaptive Fuzzy Switching Median Filterfor Universal Impulse Noise Reduction" IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, November 2010.
  27. Subhojit Sarker ,Shalini Chowdhary ,Samanwita laha and Debika Dey "Use of Non Local Means Filter to Denoise Image Corrupted by salt and pepper noise " Signal & Image Processing : An International Journal (SIPIJ) ,Vol . 3 ,No. 2,April 2012.
  28. J. Dinesh Peter, V. K. Govindan, and A. T. Mathew, "Robust estimation approach for nonlocal-means denoising based on structurally similar patches," Int. J. Open Problems Compt. Math. , vol. 2, no. 2, June 2009.
  29. Bo Xiong and Zhouping Yin, "A Universal Denoising Framework with a new impulse Detector and Non local Means :IEEETrans. Image processing ,vol21 , no4 ,April 2012,pp1663-1675.
  30. Vincent Doré and Mohamed Cheriet, "Robust NL-Means Filter With Optimal Pixel-Wise Smoothing Parameter for Statistical Image Denoising," IEEE Trans. signal Process. , vol. 57, no. 5, pp. 1703–1716, May. 2009.
  31. Tanaphol Thaipanich, Byung Tae Oh, Ping-Hao Wu, Daru Xu and C. -C. Jay Kuo, "Improved Image Denoising with Adaptive Nonlocal Means(ANL-Means) Algorithm", The International Conference on Consumer Electronics 2010 (ICCE2010)for image denoising," IEEE Trans. Image Process. , vol. 8, no. 12, pp. 1834-1838, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Image Denoising impulse noise mixed noise NLM Filter ROR statistics.