Gray-level image enhancement using differential evolution optimization algorithm

Differential Evolution (DE) algorithm represent an adaptive search process for solving engineering and machine learning optimization problems. This paper presents an attempt to demonstrate its adaptability and effectiveness for searching global optimal solutions to enhance the contrast and detail in a gray scale image. In this paper contrast enhancement of an image is performed by gray level modification using parameterized intensity transformation function that is considered as an objective function. The task of DE is to adapt the parameters of the transformation function by maximizing the objective fitness criterion. Experimental results are compared with other enhancement techniques, viz. histogram equalization, contrast stretching and particle swarm optimization (PSO) based image enhancement techniques.