GRAY LEVEL IMAGE ENHANCEMENT USING DUAL MUTATION DIFFERENTIAL EVOLUTION

Differential Evolution (DE) is one of the latest evolutionary algorithm applied to a continuous optimization problem. DE is a simple and well-organized scheme which outperforms other evolutionary algorithms in terms of robustness and convergence speed over real world applications. DE performance rely on its parameter setting. During the entire search space, these parameters remain constant, so setting them accurately is not a simple task and depends completely on the problem characteristic. A number of methods have been proposed to overcome this challenge by fine tuning the parameters. In this paper, a variation of dual mutation DE for application of image contrast enhancement is introduced. The objective of proposed work is to enhance the contrast and details of the image by increasing the fitness criterion. Here fitness criterion considers entropy and edge information of the image. Results are compared with other enhancement techniques, viz. Histogram Equalization (HE), Linear Contrast Stretching (LCS) and Differential Evolution (DE) based techniques in terms of fitness function value, the number of pixels in the edges, DV, BV which showed that the proposed DE yields better results.