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Impulse Noise Removal using Cloud Model based Filter

by Y. Bibula Flency Dhas, S. Murugappriya, G. R. Suresh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 23
Year of Publication: 2013
Authors: Y. Bibula Flency Dhas, S. Murugappriya, G. R. Suresh
10.5120/11226-6445

Y. Bibula Flency Dhas, S. Murugappriya, G. R. Suresh . Impulse Noise Removal using Cloud Model based Filter. International Journal of Computer Applications. 65, 23 ( March 2013), 30-34. DOI=10.5120/11226-6445

@article{ 10.5120/11226-6445,
author = { Y. Bibula Flency Dhas, S. Murugappriya, G. R. Suresh },
title = { Impulse Noise Removal using Cloud Model based Filter },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 23 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number23/11226-6445/ },
doi = { 10.5120/11226-6445 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:20:40.952565+05:30
%A Y. Bibula Flency Dhas
%A S. Murugappriya
%A G. R. Suresh
%T Impulse Noise Removal using Cloud Model based Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 23
%P 30-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents image processing, enhancement and restoration of images with the aid of Cloud Model (CM) filter capable of removing impulse noise. In this method, a modified decision based unsymmetrical trimmed median algorithm is used to remove noise pixels which are detected by CM based detector. The performance of CM filter is compared with that of Standard Median (SM), Adaptive Median (AM) and Wiener filters. The PSNR study is conducted at different noise levels ranging from 10% to 90% to show the effectiveness of the proposed filter.

References
  1. Zhe Zhou, "Cognition and removal of impulse noise with uncertainty" IEEE transactions on image processing, vol. 21, no. 7, July 2012.
  2. S. Esakkirajan,T. Veerakumar, "Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter", IEEE signal processing letters, Vol. 18, no. 5, May 2011.
  3. S. Kalavathy and R. M. Suresh, "A Switching Weighted Adaptive Median Filter for Impulse Noise Removal" in International Journal of Computer Applications (0975 – 8887) Volume 28– No. 9, August 2011.
  4. V. Saradhadevi, Dr. V. Sundaram, "An Adaptive Fuzzy Switching Filter for Images Corrupted by Impulse Noise" in Global Journal of Computer Science and Technology journals, Volume 11 Issue 4 Version 1. 0 March 2011.
  5. H. Chen and B. Li, "Qualitative rules mining and reasoning based on cloud model," in Proc. IEEE Int. Conf. Softw. Eng. Data Min. , Jun. 2010, pp. 523–526.
  6. Y. Gao, "An optimization algorithm based on cloud model," in Proc. IEEE Int. Conf. Comput. Intell. Security, Dec. 2009, vol. 2, pp.
  7. Y. Li and Y. Du, "Artificial Intelligent With Uncertainty". Boca Raton, FL: CRC Press, 2007.
  8. P. -E. Ng and K. -K. Ma, "A switching median filter with boundary discriminative noise detection for extremely corrupted images," IEEE Trans. Image Process. , vol. 15, no. 6, pp. 1506–1516, Jun. 2006.
  9. R. H. Chan, C. -W. Ho, and M. Nikolova, "Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization," IEEE Trans. Image Process. , vol. 14, no. 10, pp. 1479–1485, Oct. 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Model (CM) Image processing Impulse noise PSNR study

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