Using a Neuro-Fuzzy Network for Impulsive Noise Suppression from Highly Distorted Images of WEB-TVs

  • Pınar Çivicioğlu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3528)


This paper introduces a novel approach for denoising the images corrupted by Impulsive Noise (IN) by using a new nonlinear IN suppression filter, entitled t-nearest neighborhood pixels based Adaptive-Fuzzy Filter (t-AFF). The proposed filter is based on statistical impulse detection and nonlinear filtering which uses Adaptive Neuro-Fuzzy Inference System as a missed data interpolant over the t-nearest neighbor pixels of the corrupted pixels. The impulse detection is realized by using the well-known Edgington’s goodness-of-fit test which yields a decision about the impulsivity of each pixel. To demonstrate the capability of t-AFF, extensive simulations were realized revealing that the proposed filter achieves a better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, even when the images are highly corrupted by IN.


Impulse Noise Impulsive Noise Noise Density Corrupted Image IEEE Signal Processing Letter 
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  1. 1.
    Çivicioğlu, P., Alçı, M.: Impulsive Noise Suppression from Highly Distorted Images with Triangular Interpolants. AEU International Journal of Electronics and Communications 58(5), 311–318 (2004)CrossRefGoogle Scholar
  2. 2.
    Çivicioğlu, P., Alçı, M.: Edge Detection of Highly Distorted Images Suffering from Impulsive Noise. AEU International Journal of Electronics and Communications 58(6), 413–419 (2004)CrossRefGoogle Scholar
  3. 3.
    Russo, F.: Evolutionary Neural Fuzzy Systems for Data Filtering. IEEE Instrumentation and Measurement Technology Conference 2, 826–830 (1998)Google Scholar
  4. 4.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillian College Publishing Company, New York (1994)zbMATHGoogle Scholar
  5. 5.
    Jang, J.S.R.: Anfis: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Edgington, E.S.: Randomization Tests, 3rd edn. Revised and Expanded. Marcell-Deker Press, USA (1995)zbMATHGoogle Scholar
  7. 7.
    Wang, Z., Zhang, D.: Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing 46(1), 78–80 (1999)CrossRefGoogle Scholar
  8. 8.
    Yüksel, M.E., Baştürk, A.: Efficient Removal of Impulse Noise from Highly Corrupted Digital Images by a Simple Neuro-Fuzzy Operator. AEU International Journal of Electronics and Communications 57(3), 214–219 (2003)CrossRefGoogle Scholar
  9. 9.
    Chen, T., Wu, H.R.: Adaptive Impulse Detection Using Center Weighted Median Filters. IEEE Signal Processing Letters 8(1), 1–3 (2001)CrossRefGoogle Scholar
  10. 10.
    Russo, F., Ramponi, G.: A Fuzzy Filter for Images Corrupted by Impulse Noise. IEEE Signal Processing Letters. 6(3), 168–170 (1996)CrossRefGoogle Scholar
  11. 11.
    Pok, G., Liu, J.C., Nair, A.S.: Selective Removal of Impulse Noise Based on Homogeneity Level Information. IEEE Trans. on Image Process. 12(1), 85–92 (2003)CrossRefGoogle Scholar
  12. 12.
    Çivicioğlu, P., Alçı, M., Beşdok, E.: Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 383–391. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Brown, C.L., Zoubir, A.M.: Testing for Impulsive Behavior: A Bootstrap Approach. Digital Signal Processing 11(2), 120–132 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pınar Çivicioğlu
    • 1
  1. 1.Civil Aviation School, Department of Aircraft Electrics and ElectronicsErciyes UniversityKayseriTurkey

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