• DocumentCode
    2334544
  • Title

    Hybrid Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing for image contrast enhancement

  • Author

    Hoseini, Pourya ; Shayesteh, Mahrokh G.

  • Author_Institution
    Dept. of Electr. Eng., Urmia Univ., Urmia, Iran
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper, we propose a hybrid algorithm including Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Simulated Annealing (SA) metaheuristics for increasing the contrast of images. In this way, the contrast enhancement is obtained by globally transformation of the input intensities. ACO is used to generate the transfer functions which map the input intensities to the output intensities. SA as a local search method is utilized to modify the transfer functions generated by ACO. GA has the responsibility of evolutionary process of ants´ characteristics. The results indicate that the new method performs better than the previously presented methods from the subjective and objective viewpoints.
  • Keywords
    genetic algorithms; image enhancement; simulated annealing; evolutionary process; genetic algorithm; hybrid ant colony optimization; image contrast enhancement; local search method; simulated annealing metaheuristics; transfer functions; Ant colony optimization; Biological cells; Cities and towns; Histograms; Simulated annealing; Tires; Transfer functions; Ant Colony Optimization; Genetic Algorithm; Hybrid Metaheuristics; Image Contrast Enhancement; Image Processing; Simulated Annealing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5586542
  • Filename
    5586542