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Hybrid ant colony optimization, genetic algorithm, and simulated annealing for image contrast enhancement

Hoseini, P ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/CEC.2010.5586542
  3. Publisher: 2010
  4. Abstract:
  5. 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
  6. Keywords:
  7. Genetic algorithm ; Ant colony optimization ; Contrast enhancement ; Contrast of image ; Evolutionary process ; Hybrid algorithms ; Hybrid ant colony optimization ; Hybrid metaheuristics ; Image contrast enhancement ; Local search method ; Meta heuristics ; Output intensity ; Artificial intelligence ; Genetic algorithms ; Heuristic algorithms ; Image processing ; Transfer functions ; Simulated annealing
  8. Source: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 18 July 2010 through 23 July 2010, Barcelona ; 2010 ; 9781424469109 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5586542