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Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing

Hoseini, P ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1016/j.dsp.2012.12.011
  3. Publisher: 2013
  4. Abstract:
  5. In this paper, we propose a hybrid algorithm including Genetic Algorithm (GA), Ant Colony Optimisation (ACO), and Simulated Annealing (SA) metaheuristics for increasing the contrast of images. In this way, contrast enhancement is obtained by global transformation of the input intensities. Ant colony optimisation is used to generate the transfer functions which map the input intensities to the output intensities. Simulated annealing as a local search method is utilised to modify the transfer functions generated by ant colony optimisation. And genetic algorithm has the responsibility of evolutionary process of antsE characteristics. The employed fitness function operates automatically and tends to provide a balance between contrast and naturalness of images. The results indicate that the new method achieves images with higher contrast than the previously presented methods from the subjective and objective viewpoints. Further, the proposed algorithm preserves the natural look of input images
  6. Keywords:
  7. Ant colony optimisation ; Contrast Enhancement ; Evolutionary process ; Fitness functions ; Global transformation ; Hybrid algorithms ; Hybrid metaheuristics ; Local search method ; Artificial intelligence ; Heuristic algorithms ; Image processing ; Simulated annealing ; Transfer functions ; Genetic algorithms
  8. Source: Digital Signal Processing: A Review Journal ; Volume 23, Issue 3 , 2013 , Pages 879-893 ; 10512004 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S1051200412003107