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
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;
Conference_Titel :
Evolutionary Computation (CEC), 2010 IEEE Congress on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6909-3
DOI :
10.1109/CEC.2010.5586542