Neural Evolution Model for Gray Level Image Restoration

  • David Zhang
  • Xiaobo Li
  • Zhiyong Liu
Part of the The International Series on Asian Studies in Computer and Information Science book series (ASIS, volume 11)


This chapter introduces a novel image restoration method by using modified evolution strategy (MES). We first review the background of image restoration and some useful concepts associated with it. A MSE image restoration model is made in Section 4.2. Three main improvements that consist of coordinate descending mutation, “survival of the fittest” selection rule and hybrid evolution strategies are presented in Section 4.3. Neural evolution algorithm is introduced in Section 4.4. Theoretical analysis and experimental results in Section 4.5 demonstrate the efficiency of the proposed method. In Section 4.6, we draw a conclusion.


Image Restoration Gray Level Image Degraded Image Evolution Strategy Hopfield Network 
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.


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  1. [1]
    Y.T. Zhou, R. Chellappa, A. Vaid and B.K. Jenkins, “Image Restoration Using a Neural Network”IEEE trans. on ASSP36(7):1141–1151, 1988.MATHCrossRefGoogle Scholar
  2. [2]
    D.B. FogelEvolutionary Computation: Toward a New Philosophy of Machine IntelligenceIEEE Press, Piscataway, 1995.Google Scholar
  3. [3]
    T. Back, U. Hammel and H.P. Schwefel, `Evolutionary Computation: Comments on the history and Current State“IEEE Trans. On Evolutionary Computation1(1):3–16, 1997.CrossRefGoogle Scholar
  4. [4]
    W.K. PrattDigital Image ProcessingJohn Wiley & Sons, 2nd ed., New York, 1973.Google Scholar
  5. [5]
    D.B. Fogel and J.W. Atmar, “Comparing genetic operators with Gaussian mutations in simulated evolutionary process using linear systems”Biological Cybernetics63:111–114, 1990.CrossRefGoogle Scholar
  6. [6]
    R. Chellappa (ed.)Digital Image Processing IEEE Computer Society Press1992.Google Scholar
  7. [7]
    R.C. GonzalezDigital Image ProcessingAddison-Wesley,1992.Google Scholar
  8. [8]
    J.C. RussThe Image Processing HandbookRoca Raton: CRC Press,1995.Google Scholar
  9. [9]
    D. ZhangNeural Network System Design MethodologyTsinghua University Press, 1996.Google Scholar
  10. [10]
    G.A. Carpenter and S. GrossberNeural Networks for Vision and Image ProcessingThe MIT Press, Cambridge, 1992.Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • David Zhang
    • 1
  • Xiaobo Li
    • 2
  • Zhiyong Liu
    • 3
  1. 1.Hong Kong Polytechnic UniversityHong Kong
  2. 2.University of AlbertaCanada
  3. 3.National Natural Science Foundation of ChinaChina

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