Efficient Distortion Reduction of Mixed Noise Filters by Neuro-fuzzy Processing

  • M. Emin Yüksel
  • Alper Baştürk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


A simple method for reducing undesirable distortion effects of mixed noise filters for digital images is presented. The method is based on a simple 2-input 1-output neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images generated on a computer. The method can be used with any type of mixed noise filters since its operation is completely independent of the filter. The proposed method is applied to two representative mixed noise filters from the literature under different noise conditions and image properties. Results indicate that the proposed method may efficiently be used with any type of mixed noise filters to effectively reduce their distortion effects.


Training Image Impulse Noise Noise Density Consequent Parameter Premise Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Breveglieri, L., Piuri, V.: Digital median filters. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 31, 191–206 (2002)zbMATHGoogle Scholar
  2. 2.
    Umbaugh, S.E.: Computer Vision and Image Processing. Prentice-Hall International Inc., Upper Saddle River (1998)Google Scholar
  3. 3.
    Lin Jr., H., Willson, A.N.: Median filters with adaptive length. IEEE Trans. on Circuits Syst. 35, 675–690 (1998)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Sun, T., Neuvo, Y.: Detail-preserving median based filters in image processing. Pattern Recognition Letters 15(4), 341–347 (1994)CrossRefGoogle Scholar
  5. 5.
    Bovik, A.C., Huang, T.S., Munson Jr., D.C.: A generalization of median filtering using linear combinations of order statistics. IEEE Trans. on Acoust., Speech, Signal Processing ASSP–31(6), 1342–1349 (1983)CrossRefGoogle Scholar
  6. 6.
    Hardie, R.C., Barner, K.E.: Rank conditioned rank selection filters for signal restoration. IEEE Trans. on Image Processing. 3, 192–206 (1994)CrossRefGoogle Scholar
  7. 7.
    Pok, G., Liu, J., Nair, A.S.: Selective removal of impulse noise based on homogeneity level information. IEEE Trans. on Image Processing 12, 85–92 (2003)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. Int. J. Electron. Commun. 57(3), 214–219 (2003)CrossRefGoogle Scholar
  9. 9.
    Yüksel, M.E., Beşdok, E.: A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images. IEEE Transactions on Fuzzy Systems 12(6), 854–865 (2004)CrossRefGoogle Scholar
  10. 10.
    Yüksel, M.E., Baştürk, A., Beşdok, E.: Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network. EURASIP Journal on Applied Signal Processing 2004(16), 2451–2461 (2004)zbMATHCrossRefGoogle Scholar
  11. 11.
    Yüksel, M.E., Baştürk, A.: A simple generalized neuro-fuzzy operator for efficient removal of impulse noise from highly corrupted digital images. Int. J. Electron. Commun. 59(1), 1–7 (2005)CrossRefGoogle Scholar
  12. 12.
    Russo, F.: Noise removal from image data using recursive neurofuzzy filters. IEEE Trans. on Instrumentation Measurement 49(2), 307–314 (2000)CrossRefGoogle Scholar
  13. 13.
    Jang, J.-S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River (1997)Google Scholar
  14. 14.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15, 116–132 (1985)zbMATHGoogle Scholar
  15. 15.
    Sugeno, M., Kang, G.T.: Structure identification of fuzzy model. Fuzzy Sets and Systems 28, 15–33 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Kim, J., Kasabov, N.: HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks 12, 1301–1319 (1999)CrossRefGoogle Scholar
  17. 17.
    Jang, J.-S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. on Systems, Man, and Cybernetics 23, 665–685 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Emin Yüksel
    • 1
  • Alper Baştürk
    • 1
  1. 1.Digital Signal and Image Processing Lab., Dept. of Electrical and Electronics Eng.Erciyes UniversityKayseriTurkey

Personalised recommendations