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

Performance Study of Novel Median Filter for Image De-Noising

by Geeta Hanji, M.v.latte
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 23
Year of Publication: 2015
Authors: Geeta Hanji, M.v.latte
10.5120/21863-5187

Geeta Hanji, M.v.latte . Performance Study of Novel Median Filter for Image De-Noising. International Journal of Computer Applications. 122, 23 ( July 2015), 8-13. DOI=10.5120/21863-5187

@article{ 10.5120/21863-5187,
author = { Geeta Hanji, M.v.latte },
title = { Performance Study of Novel Median Filter for Image De-Noising },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 23 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number23/21863-5187/ },
doi = { 10.5120/21863-5187 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:18.279301+05:30
%A Geeta Hanji
%A M.v.latte
%T Performance Study of Novel Median Filter for Image De-Noising
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 23
%P 8-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Most common method of clearing image noise is through median filtration. Median smoothers discard outliers (or impulses) quite effectively at lower noise densities but, fail to provide adequate smoothing for heavily noised images for the simple reason that, in the conventional median filter approaches, each and every pixel of an image is filtered without concern about healthy pixels. To suppress this deficiency, in this paper, an improved median filter by the name Novel Median Filter (NMF) is proposed to enhance the effectiveness of the median filter at higher noise situations to provide a detail preserving de-noising of digital images. Proposed NMF algorithm is a 3-stage filter; first two stages are dedicated to selective switching median filtration in which noisy pixels are first detected and then the filtering operation is applied to restore their values with a valid (or a noise free) median computed for each 3×3 window position. Third level processing enables to remove any distortions present in the de-noised image. The proposed method, Novel Median Filter (NMF) is evaluated for its effectiveness in detail preserving de-noising capability on a large number of digital grey and color images affected with impulsive and non-impulsive noise in terms of objective and subjective quality measures. To better appraise the efficacy of the proposed method, Novel Median Filter (NMF), MATLAB simulation results in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and the de-noised images are obtained and are compared with those obtained for a standard median filter (SMF) and its few variants and meaningful conclusions are derived. Detail preservation capability of the proposed filter has been demonstrated through experimentation on medical samples.

References
  1. Gonzalez R. C. and Wood R. E. : Digital Image Processing, PHI, Second Edition, (2007).
  2. Pitas I. and Venetsanopoulos A. N. : Nonlinear Digital Filters: Principles and Applications, Boston, MA: Kluwer Academic, (1999).
  3. Bovik A. C: Hand book of Image and Video Processing, Academic Press, (2000).
  4. Ataman E. , Aatre V. K. and Wong K. M. , Some Statistical Properties of Median Filters, IEEE Trans. Acoust. , Speech and Signal Processing, Vol. 29, No. 5, (1981), pp. 1073-1075.
  5. Nodes T. A. and Gallagher N. C. , Median Filters: Some Modifications and Their Properties, IEEE Trans. Acoust. , Speech and Signal Processing, Vol. 30, No. 5, (1984), pp. 739-746.
  6. Bovik A. C. , Huang T. S. , and Munson. D. C. , The Effect of Median Filtering on Edge Estimation and Detection, IEEE Trans. Pattern Anal. Machine Intelligence, Vol. PAMI-9, No. 2, (1987), pp. 181-194.
  7. Tukey J. W. : Exploratory Data Analysis Reading, Addison Wesley. in, (1971).
  8. Sin Hoong Teoh, Boon Tatt Koik, and Haidi Ibrahim, Exploration of Current Trend on Median Filtering Methods Utilized in Digital Grayscale Image Processing, International Journal of Materials, Mechanics and Manufacturing, Vol. 1, No. 1, (2013).
  9. Nieminen A. , Heinonen P. , and Neuvo Y. , A New Class of Detail Preserving Filters for Image Processing, IEEE Trans. Pattern Anal. Machine Intell. , Vol. PAMI-9, No. 1, (1987), pp. 74-90.
  10. Arce G. R. and Foster R. E. , 'Detail Preserving Ranked-Order based Filters for Image Processing', IEEE Trans. Acoustic. , Speech, Signal Process. , Vol. 37, No. 1, (1989), pp. 83-98.
  11. Brownrigg D. R. K. , The Weighted Median Filter, Commun. ACM, Vol. 27, No. 8, (1984), pp. 807-818,.
  12. Yin L. , Yang R. , Gabbouj M. and Neuvo Y. , Weighted median filters: A Tutorial, IEEE Trans. Circuits and Systems-II: Analog and Digital Signal Processing, Vol. 43, No. 3, (1996), pp. 157-192.
  13. Astola J. and Kuosmanen P. : Fundamentals of Nonlinear Digital Filtering: Boca Raton, FL: CRC (1997),
  14. Ko, S. J. , and Lee, Y. H. , Center Weighted Median Filters and Their Applications to Image Enhancement,
  15. IEEE Trans. Circuits and Systems, vol. 38, (1991), No. 9, pp. 984–993.
  16. H. Hwang and R. A. Haddad, Adaptive Median Filters: New Algorithms and Results, IEEE Trans. Image Processing, Vol. 4, (1995), pp. 499– 502.
  17. Tao Chen and Hong Ren, Space Variant Median Filters for the Restoration of Impulse Noise Corrupted Images, IEEE Trans. Circuits and Syst-II, Analog and Digital Signal Processing, Vol. 48, No. 8, (2001), pp. 784-789.
  18. Wang Z. and Zhang D. , Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images, IEEE Trans. on Circuits and Systems II, Vol. 46, No. 3, (1999), pp. 78 - 80.
  19. T. Chen and H. R. Wu, Adaptive Impulse Using Center-Weighted Median Filters, IEEE Signal Processing Letters, (2001), pp. 1-3.
  20. Kwame Boateng, Ben Weyori Asubam and David Sanka Laar, Improving the Effectiveness of the Median Filter, International Journal of Electronics and Communication Engineering, Vol. 5, No. 1, (2012), pp. 85-97.
  21. Pragati Agarwal, Jayendra Singh Verma, A Survey of Linear and Non-Linear Filters for Noise Reduction, International Journal of Advanced Research in Computer Science and Management Studies ,Vol. 1,(2012).
  22. Sonali R. Mahakale, Nilesh Singh V. T. , A Comparative Study of Image Filtering on Various Noisy pixels, International Journal of Image Processing and Vision Sciences, Vol. 1, (2012).
  23. Priyanka Kamboj, Versha Rani, Brief Study of Various Noise Model and Filtering Techniques, Journal of Global Research in Computer Science, Volume 4, No. 4, (2013).
  24. S. Zhang and M. A. Karim, A New Impulse Detector for Switching Median Filters, IEEE Signal Process. Letters,. Vol. 9, No. 11, (2002), pp. 360–363.
  25. Geeta Hanji, M. V. Latte, A New Impulse Noise Detection and Filtering Algorithm, Image Processing & Communications (IPC), Vol. 16, No. 1-2, (2012), pp. 43-48.
  26. Chung-Chia Kang, Wen-June Wang, Modified Switching Median Filter with One More for Detector for Impulse Noise Removal, Int. J. Electron Commun. (AEU), Vol. 63, (2009), pp. 998-1004.
Index Terms

Computer Science
Information Sciences

Keywords

Novel Median Filter (NMF) Impulse Noise Gaussian Noise Valid Median.

Powered by PhDFocusTM