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Reseach Article

Fuzzy Logic Decision based Adaptive Directional Weighted Median Filter for Restoring Impulse Corrupted Images

by S. Abdul Saleem, T. Abdul Razak
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 13
Year of Publication: 2016
Authors: S. Abdul Saleem, T. Abdul Razak
10.5120/ijca2016909565

S. Abdul Saleem, T. Abdul Razak . Fuzzy Logic Decision based Adaptive Directional Weighted Median Filter for Restoring Impulse Corrupted Images. International Journal of Computer Applications. 141, 13 ( May 2016), 8-13. DOI=10.5120/ijca2016909565

@article{ 10.5120/ijca2016909565,
author = { S. Abdul Saleem, T. Abdul Razak },
title = { Fuzzy Logic Decision based Adaptive Directional Weighted Median Filter for Restoring Impulse Corrupted Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 13 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume141/number13/24842-2016909565/ },
doi = { 10.5120/ijca2016909565 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:25.429646+05:30
%A S. Abdul Saleem
%A T. Abdul Razak
%T Fuzzy Logic Decision based Adaptive Directional Weighted Median Filter for Restoring Impulse Corrupted Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 13
%P 8-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image restoration and enhancement are the major research areas in digital image processing. The main objective of image restoration is to reduce noise and improve resolution loss on digital images in any real-time domains. Many images like photographs, medical images, satellite images and aerial images suffer from poor contrast and noises due to various reasons such as lightening, bad weather or flaw in the equipment. It is necessary to restore the image by removing impulse noises and to increase the image quality by using image parameters. Number of image restoration filters have been introduced in the past decades and tested on standard images to prove their efficiency. This study proposes a new fuzzy logic decision based adoptive directional weighted median filter for the restoration of impulse corrupted digital images. The proposed filter includes fuzzy logic based decision to model the uncertainties, while detecting and correcting impulses. The proposed correction scheme provides weight to the uncorrupted pixels that show much similarity with other uncorrupted pixels in the 3×3 kernel window while replacing impulses. The proposed fuzzy filter adapts to various noise level and image conditions and is capable of suppressing noise while preserving image details. The experimental outcome in terms of subjective and objective metrics favour the proposed algorithm than many other major filters in the literature.

References
  1. Rafael C. Gonzalez, and Richard E.Wood. 2009. “Digital Image Processing”, 3rd Edition, Prentice-Hall.
  2. Wang, Z., Zhang, D.,1998, ‘Restoration of Impulse Noise Corrupted Images using Long-Range Correlation’, IEEE Signal Process. Lett., vol. 5, No. 1, pp. 4–7
  3. Wang, Z., Zhang, D.,1999, ‘Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images’, IEEE Trans. Circuits Syst.-II, Analog Digit. Signal Process., 46, (1), pp. 78–80
  4. Ko, S.J. and Lee, Y.H. 1991, “Center weighted median filters and their applications to image enhancement”, IEEE Trans. Circuits Syst., 38, pp. 984–993
  5. Hwang H. and Hadded R.A., 1995, “Adaptive Median Filter: New algorithms and results”,IEEE Trans.Image Process, vol. 4, no. 4, 1995, pp. 499– 502.
  6. Kwame Osei Boating, Benjamin Weyori Asubam and David Sanka Laar, 2012 “Improving the Effectiveness of the Median Filter”, International Journal of Electronic and Comunication Engineering,
  7. Zhang, X., Xiong, Y.,2009, ‘Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter’,IEEE Signal Process. Lett., 16, (4), pp. 295–298
  8. Nallaperumal, K., Varghese, J., Saudia, S., 2006, ‘Salt & Pepper Impulse Noise Removal using Adaptive Switching Median Filter’. Proc. Conf. of Asia Pacific OCEANS’06, Singapore, pp. 1–8
  9. Kenny Kal Vin Toh, K., Mat Isa, N A., 2000, ‘Noise Adaptive Fuzzy Switching Median Filter for Salt and Pepper Noise Reduction’, IEEE Signal Process. Lett., 17, (3), pp. 281–284
  10. Fitri, U., Uchimura, K., Koutaki, G.,2012, ‘High Density Impulse Noise Removal by Fuzzy Mean Linear Aliasing Window Kernel’, IEEE Int. Conf. Signal Process. Commun. Comput., pp. 711–716
  11. Srinivasan, K.S., and Ebenezer D., 2007, ‟A new fast and efficient decision based algorithm for removal of high density impulse noise”, IEEE signal processing. vol. 14, no.3, pp. 189-192.
  12. Nair Madhu S., Revathy K. and Tatavarti Rao, 2008, “Removal of Salt and Pepper Noise in Images: A new Decision-Based Algorithm ”Proceeding of International Multi Conference of Engineers and Computer Scientists, vol. 1, pp. 19-21.
  13. Soruba Marcell, J., Jayachandran, A., and Kharmega Sundararaj, G. 2012. “An Efficient Algorithm for Removal of Impulse Noise using Adaptive Fuzzy Switching Weighted Median Filter”, International Journal of Computer Technology and Electronics Engineering(IJCTEE), vol.2, issue 2.
  14. Abdul Saleem,S., Abdul Razak,T., 2015,”Fuzzy Logic Decision Based Effective Adaptive Median Filter for Removing High Density Impulse noises in Digital Images”, vol. 10 No.82, pp. 84-90
  15. Xu, H., Zhu, G., Peng, H., Wang, D.,2004, ‘Adaptive Fuzzy Switching Filter for Images Corrupted by Impulse Noise’, Pattern Recognit. Lett., Vol. 25, (15), pp. 1657–1661
  16. Fabijanska, A., Sankowski, D.,2011, ‘Noise Adaptive Switching Median Based Filter for Impulse Noise Removal from Extremely Corrupted Images’, IET Image Process., vol. 5, (5), pp. 472–480
Index Terms

Computer Science
Information Sciences

Keywords

Gaussian noise salt & pepper noise image parameters edge preservation fuzzy logic.