Fast and reliable image enhancement using fuzzy relaxation technique

  • Hua Li
  • Hyun S. Yang
Image Restoration And Enhancement
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


In this paper, we propose a fuzzy relaxation technique that exploits fuzzy membership functions for gray level transformation. This technique enhances image contrast very effectively and expeditiously; different order of fuzzy membership functions and different rank statistics are tried to improve the enhancement speed and quality, respectively. We provide the proof of the convergence of our relaxation algorithm, and illustrate some experimental results.


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6. References

  1. [1]
    A. Barr and E. Feigenbaum, Handbook of Artificial Intelligence, Vol. 3, William Kaufmann, Inc., 1982.Google Scholar
  2. [2]
    V. Goetcherian, “From Binary to Gray Tone Image Processing Using Fuzzy Logic Concepts,” Pattern Recognition, Vol. 12, pp. 7–15, 1980.CrossRefGoogle Scholar
  3. [3]
    T. L. Huntsberger et. al., “Interactive Fuzzy Image Segmentation,” Pattern Recognition, Vol. 18, No. 2, pp. 131–138, 1985.CrossRefGoogle Scholar
  4. [4]
    A. Kandel, Fuzzy Mathematical Techniques with Applications, Addision-Wesley, 1986.Google Scholar
  5. [5]
    A. Kaufmann and M. Gupta, Introduction to fuzzy Arithmetic, Van Nonstrand Reinhold Co. Inc., 1985.Google Scholar
  6. [6]
    Y. Nakagawa and A. Rosenfeld, “A Note on the Use of Local Min and Max Operations in Digital Picture Processing,” IEEE SMC-8, Vol. 11, pp. 632–635, 1978.Google Scholar
  7. [7]
    Y. Nakagawa and A. Rosenfeld, “Some Experiments On Variable Thresholding,” Pattern Recognition, Vol. 11, pp. 191–204, 1979.CrossRefGoogle Scholar
  8. [8]
    K. Pal and Robert A. King, “On Edge Detection of X-ray Images Using Fuzzy Set,” IEEE PAMI-5, No. 1, pp. 69–77, 1983.Google Scholar
  9. [9]
    A. Rosenfeld, “The Fuzzy Geometry of Image Subsets,” Pattern Recognition, Vol. 2, pp. 311–317, 1984.Google Scholar
  10. [10]
    L. Vanderheydt, F. Dom, et. al., “Two Dimensional Shape Decomposition Using Fuzzy Set Theory Applied to Automated Chromosome Analysis,” Pattern Recognition, Vol. 13, PP. 147–157, 1981.Google Scholar
  11. [11]
    W. K. Pratt: Digital Image Processing, Part 4, John Wiley & Sons, 1978.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Hua Li
    • 1
  • Hyun S. Yang
    • 1
  1. 1.Dept. of Electrical and Computer EngineeringUniversity of IowaIowa CityU.S.A.

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