Abstract

Hybrid metaheuristic, an advancement over classical metaheuristic, provides a more effective search methodology. It combines several metaheuristic algorithms into one optimization mechanism. In this paper image enhancement is considered as an optimization problem. Hybrid metaheuristic techniques are used to find the optimum value for a set of parameters of a transformation function, with an aim towards maximizing a fitness function. Three hybrid metaheuristic approaches are employed to find the optimum solution. Results of all three algorithms are compared amongst themselves. Comparison is also shown with classical metaheuristic algorithms and traditional enhancement approach of histogram equalization.

References

• A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1991.
• F. Saitoh, Image contrast enhancement using Genetic Algorithm, in Proc. IEEE SMC, Tokyo, Japan, 1999, pp. 899–904.
• Jin-Hyuk Hong, Sung-Bae Cho, Ung-Keun Cho, A Novel Evolutionary Approach to Image Enhancement Filter Design: Method and Applications, IEEE Transaction on System, Man and Cybernetics-Part B: Cybernetics, Vol. 39, No. 6, Dec 2009
• ApurbaGorai, AshishGhosh, Gray-level Image Enhancement By Particle Swarm Optimization, World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)
• Qingyun Yang, An Adaptive Image Contrast Enhancement based on Differential Evolution, 3rd International Congress on Image and Signal Processing , (CISP2010)
• C. Munteanu, V. Lazarescu, Evolutionary contrast stretching and detail enhancement of satellite images, In Proc. Mendel, Berno, Czech Rep. , pp. 94-99, 1999.
• Yen-Wei Chen, TatsuroEnokura, and ZenshoNakao, A Fast Image Restoration Algorithm Based on Simulated Annealing, Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, 31' Aug-1- Sept 1999, Adelaide, Australia
• C. Blum, M. J. Blesa Aguilera, A. Roli, and M. Sampels, editors. Hybrid Metaheuristics – An Emerging Approach to Optimization, volume 114 of Studies in Computational Intelligence. Springer, 2008.
• Bo Liu, PeishengMeng, Hybrid Algorithm Combining Ant Colony Algorithm with Genetic Algorithm for Continuous Domain, The 9th International Conference for Young , Computer Scientists, 2008
• Yi-Tung Kao, ErwieZahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions, Applied Soft Computing 8 (2008) 849–857
• F. Focacci, F. Laburthe, and A. Lodi. Local search and constraint programming. In Glover and Kochenberger
• , pages 369–403.
• El-GhazaliTalbi, Metaheuristic: from design to implementation, John Wiley & Sons
• CristianMunteanu , Agostinho Rosa, Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution, IEEE Transaction on System, Man and Cybernetics-Part B: Cybernetics, Vol. 34, No. 2, April 2004
• J. Holland, Adaptation in Natural and Artificial systems, University of Michigan Press, Ann Anbor, 1975.
• J. Kennedy and R. Eberhart, Particle swarm optimization, in: Proc. of the IEEE Int. Conf. on Neural Networks, Piscataway, NJ, p. 1942-1948 (1995).
• R. Storn and K. Price. Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Int CS Institute, University of California, Mar 1995.
• S. Kirkpatrick, C. D. Gellat, and M. P. Vecchi, Optimization by simulated annealing, Science, 220, 671-680 (1983).
• Nourani, Y. and Andresen, B. (1998) A comparison of simulated annealing cooling strategies. Journal of Physics A—Mathematical and General, 31, 8373–8385.
• Michalewicz Z. (1996) "Genetic Algorithms + Data Structures = Evolution Programs", Springer Verlag.