Advertisement

A Fuzzy Logic Control Based Approach for Image Filtering

  • Farzam Farbiz
  • Mohammad Bagher Menhaj
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 52)

Summary

This chapter is devoted to introduce a new filtering approach based on fuzzy-logic control concepts with the properties of removing impulsive noise and smoothing out Gaussian noise while, simultaneously, preserving edges and image details efficiently. The main idea behind the proposed filtering approach is that each pixel is not allowed to be uniformly fired by each of the fuzzy rules. In this chapter, different modifications of this filtering approach (Iterative Fuzzy Control based Filter — IFCF) named by MIFCF, EIFCF, SFCF, SSFCF, FFCF, AFCF and ACFCF are presented along with some test experiments highlighting the merit of each filter. From the experimental results we may list the concluding remarks of the proposed filtering approach: high quality of edge preserving ability, high filtering quality especially for complex images, multiplicative noise removing property for IFCF based filters, floating point free calculations and very fast performance for FFCF based filters.

Keywords

Membership Function Mean Square Error Gaussian Noise Fuzzy System Fuzzy Rule 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arakawa K., Digital signal processing based on fuzzy rules,in: “Proceedings of the Fifth IFSA World Congress”, pp. 1305–1308, 1994Google Scholar
  2. 2.
    Bezdek J.C. and Pal S.K., “Fuzzy Models for Pattern Recognition”, IEEE Press, New York, 1992Google Scholar
  3. 3.
    Chen B.T., Chen Y. and Hsu W., Image processing and understanding based on the fuzzy inference approach, in: “Proceedings of FUZZ-IEEE’94 — 3rd IEEE Int. Conf. Fuzzy Systems”, pp. 279–83, 1994Google Scholar
  4. 4.
    Chen C.L., Lee C.S. and Kuo Y.H., Design of high speed weighted fuzzy mean filters with generic LR fuzzy cells, in: “Proceedings of ICIP’96 — 3rd IEEE Int. Conf. Image Processing”, Vol. 2, pp. 1027–1030, 1996CrossRefGoogle Scholar
  5. 5.
    Choi Y. and Krishnapuram R., A robust approach to image enhancement based on fuzzy logic, IEEE Trans. Image Processing, Vol. 6, No. 6, pp. 808–825, 1997Google Scholar
  6. 6.
    Doroodchi M. and Reza A.M., Fuzzy cluster filter,in: “Proceedings of ICIP’96 — 3rd IEEE Int. Conf. Image Processing”, Vol. 2, pp. 939–942, 1996Google Scholar
  7. 7.
    Doroodchi M. and Reza A.M., Implementation of fuzzy cluster filter for nonlinear signal and image processing, in: “Proceedings of ICIP’96 — 3rd IEEE Int. Conf. Image Processing”, Vol. 3, pp. 2117–2122, 1996Google Scholar
  8. 8.
    Haralick R.M. and Shapiro L.G., “Computer and Robot Vision, Volume 1”,Addison Weseley, 1992Google Scholar
  9. 9.
    Jain A.K., “Fundamentals of digital image processing”,Prentice-Hall, 1989Google Scholar
  10. 10.
    Kim J.S. and Cho H.S., A fuzzy logic and neural network approach to boundary detection for noisy imagery, Fuzzy Sets and Systems, Vol. 65, No. 2/3, pp. 141–159, 1994Google Scholar
  11. 11.
    Kosko B., “Neural Networks and Fuzzy Systems”, Prentice-Hall, 1992Google Scholar
  12. 12.
    Krishnapuram R. and Keller M., Fuzzy sets theoretic approach to computer vision: an overview, in: “Proceedings of FUZZ-IEEE’92 — First IEEE Int. Conf. Fuzzy Systems”, pp. 135–142, 1992Google Scholar
  13. 13.
    Lee C.S., Kuo Y.H. and Yu P.T., Weighted fuzzy mean filters for heavy-tailed noise removal, in: “Proceedings of the Joint Third Int. Symp. Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society”, pp. 601–606, 1995Google Scholar
  14. 14.
    Mancuso M., Poluzzi R. and Rizzotto G., A fuzzy filter for dynamic range reduction and contrast enhancement, in: “Proceedings of FUZZ-IEEE’94 — 3rd IEEE Int. Conf. Fuzzy Systems”, pp. 264–267, 1994CrossRefGoogle Scholar
  15. 15.
    Mastin G.A., Adaptive filters for digital image noise smoothing: an evaluation,Computer Vision, Graphics, Image Processing, Vol. 31, pp. 103–121, 1985Google Scholar
  16. 16.
    Muneyasu M., Wada Y. and Hinamoto T., Edge-preserving smoothing by adaptive nonlinear filters based on fuzzy control laws, in: “Proceedings of ICIP’96–3rd IEEE Int. Conf. Image Processing”, Vol. 1, pp. 785–788, 1996CrossRefGoogle Scholar
  17. 17.
    Pal S.K., Fuzzy sets in image processing and recognition,in: “Proceedings of FUZZ-IEEE’92 - First IEEE Int. Conf. Fuzzy Systems”, pp. 119–126, 1992Google Scholar
  18. 18.
    Peng S. and Lucke L., A hybrid filter for image enhancement,in: “Proceedings of ICIP’95–2nd IEEE Int. Conf. Image Processing”, Vol. 1, pp. 163–166, 1995Google Scholar
  19. 19.
    Pitas I. and Venetsanopoulos A.N., “Nonlinear Digital Filters: Principles and Applications”,Kluwer Academic Publishers, 1990Google Scholar
  20. 20.
    Russo F. and Ramponi G., Edge detection by FIRE operators,in: “Proceedings of FUZZ-IEEE’94–3rd IEEE Int. Conf. Fuzzy Systems”, pp. 249–253, 1994Google Scholar
  21. 21.
    Russo F. and Ramponi G., Combined FIRE filters for image enhancement,in: “Proceedings of FUZZ-IEEE’94–3rd IEEE Int. Conf. Fuzzy Systems”, pp. 261–264, 1994Google Scholar
  22. 22.
    Russo F. and Ramponi G., Fuzzy operator for sharpening of noisy images,IEEE Electron Lett., Vol. 28, pp. 1715–1717, 1992Google Scholar
  23. 23.
    Russo F., A user-friendly research tool for image processing with fuzzy rules,in: “Proceedings of FUZZ-IEEE’92 - First IEEE Int. Conf. Fuzzy Systems”, pp. 561–568, 1992Google Scholar
  24. 24.
    Russo F. and Ramponi G., Nonlinear fuzzy operators for image processing,Signal Processing, Vol. 38, pp. 429–440, 1994Google Scholar
  25. 25.
    Russo F. and Ramponi G., A noise smoother using cascaded FIRE filters,in: “Proceedings of FUZZ-IEEE’95–4th IEEE Int. Conf. Fuzzy Systems”, Vol. 1, pp. 351–358, 1995Google Scholar
  26. 26.
    Russo F. and Ramponi G., An image enhancement technique based on the FIRE operator,in: “Proceedings of ICIP’95–2nd IEEE Int. Conf. Image Processing”, Vol. 1, pp. 155–158, 1995Google Scholar
  27. 27.
    Russo F. and Ramponi G., Removal of impulsive noise using a FIRE filter,in: “Proceedings of ICIP’96–3rd IEEE Int. Conf. Image Processing”, Vol. 2, pp. 975–978, 1996Google Scholar
  28. 28.
    Russo F. and Ramponi G., A fuzzy filter for images corrupted by impulse noise,IEEE Signal Processing Letters, Vol. 3, No. 6, pp. 168–170, 1996Google Scholar
  29. 29.
    Sucher R., A self-organizing nonlinear filter based on fuzzy clustering,in: “Proceedings of IEEE-ISCAS’96 - IEEE International Symposium on Circuits and Systems”, Vol. 2, pp. 101–103, 1996Google Scholar
  30. 30.
    Taguchi A., A design method of fuzzy weighted median filters,in: “Proceedings of ICIP’96–3rd IEEE Int. Conf. Image Processing”, Vol. 1, pp. 423–426, 1996Google Scholar
  31. 31.
    Vanzo A., Ramponi G. and Sicuranza G.L., An image enhancement technique using polynomial filters, in: “Proceedings of ICIP’94–First IEEE Int. Conf. Image Processing”, pp. 477–481, 1994CrossRefGoogle Scholar
  32. 32.
    Wang J.H. and Yu M.D., Image restoration by adaptive fuzzy optimal filter,in: “Proceedings of the IEEE Int. Conf. Systems, Man and Cybernetics, Intelligent Systems for the 21st Century”, Vol. 1, pp. 845–848, 1995Google Scholar
  33. 33.
    Yang X. and Toh P.S., Adaptive fuzzy multilevel median filter,IEEE Trans. Image Processing, Vol. 4, pp. 680–682, 1995Google Scholar
  34. 34.
    Yu P.T. and Chen R.C., Fuzzy stack filters- Their definitions, fundamental properties, and application in image processing,IEEE Trans. Image Processing, Vol. 5, No. 6, pp. 838–854, 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Farzam Farbiz
    • 1
  • Mohammad Bagher Menhaj
    • 1
  1. 1.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran

Personalised recommendations