A Fuzzy Genetic Approach to Impulse Noise Removal

  • K. K. Anisha
  • M. Wilscy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 192)


Many practical applications require analysis of digital images. An accurate analysis is possible only from an image free of noise. Image denoising with multiple image filters might produce better results than a single filter, but it is very difficult to find a set of appropriate filters and the order in which the filters are to be applied. In this paper, we propose a Fuzzy Genetic Algorithm to find the optimal filter sets for removing impulse noise from images. Here, a Fuzzy Rule Based System is used to adaptively change the crossover probability of the Genetic Algorithm used to determine the optimal sets of filters from a pool of standard image filters. Fuzzy Genetic Algorithm gives better results than conventional Genetic Algorithm. This method does not require any deep knowledge about the image noise factors; so it can be easily used in any image processing application.


Adaptive Genetic Algorithm Fuzzy Genetic Algorithm (FGA) Fuzzy Rule Based System (FRBS) Genetic Algorithm (GA) Image filters Impulse noise 


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  1. 1.
    Gonzalez, R., Woods, R.: Digital Image Processing. Addison Wesley, Reading (1992)Google Scholar
  2. 2.
    Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  3. 3.
    Hong, J.H., Cho, S.B., Cho, U.K.: A Novel Evolutionary Method to Image Enhancement Filter Design: Method and Applications. IEEE Transactions on Systems, Man and Cybernetics – Part B, Cybernetics 39(6), 1446–1457 (2009)CrossRefGoogle Scholar
  4. 4.
    Cho, U.-K., Hong, J.-H., Cho, S.-B.: Evolutionary Image Enhancement for Impulsive Noise Reduction. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 678–683. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Herrera, F., Lozano, M.: Adaptive Genetic Algorithms based on Fuzzy Techniques. In: Proceedings of the Sixth International Conference on Information Processing and Management Uncertainty in Knowledge Based Systems, pp. 775–780. IEEE, Los Alamitos (1996)Google Scholar
  6. 6.
    Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Transactions on Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  7. 7.
    Ross, T.J.: Fuzzy Logic with Engineering Applications. McGraw Hill, New York (1995)zbMATHGoogle Scholar
  8. 8.
    Herrera, F., Lozano, M.: Adaptive Genetic Operators Based on Coevolution with Fuzzy Behaviours. IEEE Transactions on Evolutionary Computation 5(2), 149–165 (2001)CrossRefGoogle Scholar
  9. 9.
    Lee, M.A., Takagi, H.: Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques. In: Proceedings of Fifth International Conference on Genetic Algorithms, Urbana – Champaign, IL, pp. 76–83 (1993)Google Scholar
  10. 10.
    Cordon, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. In: Advances in Fuzzy Systems — Applications and Theory, vol. 19. World Scientific Publishing Co. Pte. Ltd., Singapore (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • K. K. Anisha
    • 1
  • M. Wilscy
    • 1
  1. 1.Department of Computer ScienceUniversity of KeralaThiruvananthapuramIndia

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