Noise Detection and Reduction Using Algorithms Based on Particle Swarm Optimization and Fuzzy Decision Making

Volume 9, Issue 4, pp 370 - 380 http://dx.doi.org/10.22436/jmcs.09.04.13

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Authors

Mohammadreza Heydari Heydarinezhad - Islamic Azad University of Qeshm, International branch. Azar Azadi - Islamic Azad University of Qeshm, International branch.

Abstract

Noise filtering has a special importance for systems related to digital images processing. Mostly conventional filter used in image processing applications for noise cancellation is the median filter which is especially used to reduce salt and pepper noise in images. However median filter has a good performance in smoothing and noise reduction, but leads to blurring and elimination of some fine details and destruction of edges in image. One approach in confronting with conventional noise filters like median filter is not to operate same process on all pixels. This means by using a decision block or classifier, confronting with pixels must be determined appropriately. In this thesis we address to the problem of designing such decision blocks for noise reduction based on fuzzy inference theory. Another main subject in this thesis is the procedure of utilization of optimization algorithms based on swarm movement of particles for designing the fuzzy part.

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Keywords

• Filtering
• Fuzzy Set Theory
• Particle Swarm Optimization

MSC

•  68Q25
•  90C59
•  94D05

References

• [1] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965), 338-353
  • Google Scholar


• [2] J. Astola, P. Kuosmanen, Fundamentals of nonlinear digital filtering, Boca Raton, FL: CRC (1997)
  • Google Scholar


• [3] L. A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics , 28 - 44 (1973)
  • View Article
  • Google Scholar
  • MATH


• [4] E. H. Mamdani, Applications of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers, 26 (1977), 1182-1191
  • Google Scholar


• [5] R. C. Eberhart, Y. Shi, Particle swarm optimization: developments, applications and resources, in Proc. 2001 Congr. Evolutionary Computation, vol. 1 (2001.)
  • View Article
  • Google Scholar


• [6] X. Chen, Y. Li, A modified PSO structure resulting in high exploration ability with convergence guaranteed, IEEE Trans. Syst.,Man, Cybern. B, Cybern, 37 (2007), 1271–1289
  • Google Scholar


• [7] S. Kiranyaz, T. Ince, A. Yildirim, M. Gabbouj, Fractional Particle Swarm Optimization in Multi Dimensional Search Space, IEEE Transactions on Systems, Man, and Cybernetics – Part B, in Press, doi:10.1016/j.neunet.2009.05.013 (2009)
  • View Article
  • Google Scholar


• [8] S. Gupta, S. Devi, Modified PSO Algorithm with High Exploration and Exploitation Ability, International Journal of Software Engineering Research & Practices , Vol.1, Issue 1 (2011)
  • Google Scholar


• [9] Ronald R. Yager, Lotfi A. Zadeh, An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer Academic Publishers Norwell, MA (1992)
  • Google Scholar


• [10] Oscar Castillo, Patricia Melin, Particle Swarm Optimization in the Design of Type-2 Fuzzy Systems, Recent Advances in Interval Type-2 Fuzzy Systems, Springer Briefs in Applied Sciences and Technology, 1 (2012), 27-31.
  • View Article
  • Google Scholar


• [11] Zafer Bingül, Oğuzhan Karahan, A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control, Expert Systems with Applications , 38 (2011), 1017–1031.
  • View Article
  • Google Scholar