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

Detail Preserving Median Filter for High Density Impulse Noise

Published on May 2012 by Kiran P. Dange, R K Kulkarni
National Conference on Advancement in Electronics & Telecommunication Engineering
Foundation of Computer Science USA
NCAETE - Number 2
May 2012
Authors: Kiran P. Dange, R K Kulkarni
191832d6-6ce0-434a-a360-9382cbb8afdc

Kiran P. Dange, R K Kulkarni . Detail Preserving Median Filter for High Density Impulse Noise. National Conference on Advancement in Electronics & Telecommunication Engineering. NCAETE, 2 (May 2012), 13-16.

@article{
author = { Kiran P. Dange, R K Kulkarni },
title = { Detail Preserving Median Filter for High Density Impulse Noise },
journal = { National Conference on Advancement in Electronics & Telecommunication Engineering },
issue_date = { May 2012 },
volume = { NCAETE },
number = { 2 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 13-16 },
numpages = 4,
url = { /proceedings/ncaete/number2/6598-1088/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement in Electronics & Telecommunication Engineering
%A Kiran P. Dange
%A R K Kulkarni
%T Detail Preserving Median Filter for High Density Impulse Noise
%J National Conference on Advancement in Electronics & Telecommunication Engineering
%@ 0975-8887
%V NCAETE
%N 2
%P 13-16
%D 2012
%I International Journal of Computer Applications
Abstract

For removing impulse noise, basic median filter is used. The median filters are not able to retain edges and fine details of images at high density noise. A new algorithm is proposed to overcome the limitations of existing methods. This algorithm is categorized into two stages. First stage is detection of impulse pixel depends on threshold values and second stage is filtering of corrupted pixels which are replaced by the median of uncorrupted pixels in the filtering window. This proposed algorithm works good up to 90% noise density. Extensive simulations show that the proposed filter restores fairly well even the images that are highly corrupted. The performance measures like PSNR, MSE, and UQI are better than other methods.

References
  1. PITAS, I. , and VENETSANOPOU. A, 1990, "Nonlinear Digital filters: principles and application", (Kluwer, Novell, MA. )
  2. KO, S. J. , and LEE Y. H, 1991, "Center weighted median filters and their application to image enhancement", IEEE Trans. Circuits Syst, 38(3), pp190-195.
  3. YIN, L. , YANG, R. , GABBOUJ. M. , and NEUVO. Y, 1996, "Weighted median filters: a tutorial", IEEE Trans. Circuits Syst. , 43 (3), pp. 157-192.
  4. S. Kalavathy, R. M. Suresh,"A switching weighted median filter for impulse noise removal", International Journal of Computer Applications (0975 – 8887) Volume 28– No. 9, August 2011.
  5. Z. Wang, D. Zhang, "Progressive switching median filter for the removal of impulse noise from highly corrupted Images", IEEE Trans. Circuits Syst. , vol. 46, pp. 78-80, 1999.
  6. T. Chen and H. R. Wu, "Space Varient Median Filters for The Restoration of Impulse Noise Corrupted Images", IEEE Trans. Circuits Syst. -II: Analog And Digital Signal Processing, vol. 48, No. 8, pp. 784-789, 2001.
  7. T. Chen, K. -K. Ma, and Li-Hui Chen "Tri-state median filter for image denoising", IEEE Trans. Image Processing, vol. 8, pp. 1834 – 1838, 1999.
  8. Zhou Wang, Alan Conard Bovik, Hamid Rahim sheik and Erno P. Simoncelli, "Image Quality Assessments: From Error Visibility to Structural Similarity", IEEE Trans. Image Processing ,Vol. 13,2004.
  9. Gonzalez and Woods, "Digital image processing", 2nd Edition, prentice hall, 2002.
  10. Gonzalez, R. C. and Woods, Steven L. Eddins, "Digital Image Processing using MATLAB ", Prentice Hall, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Impulse Noise Threshold Median Filter