A genetic algorithm approach to color image enhancement

Abstract

Image enhancement techniques are used to improve image quality or extract the fine details in the degraded images. Most existing color image enhancement techniques usually have three weaknesses: (1) color image enhancement applied in the RGB (red, green, blue) color space is inappropriate for the human visual system; (2) the uniform distribution constraint employed is not suitable for human visual perception; (3) they are not robust, i.e., one technique is usually suitable for one type of degradations only. In this study, a genetic algorithm (GA) approach to color image enhancement is proposed, in which color image enhancement is formulated as an optimization problem. In the proposed approach, a set of generalized transforms for color image enhancement is formed by linearly weighted combining four types of nonlinear transforms. The fitness (objective) function for GAs is formed by four performance measures, namely, the AC power measure, the compactness measure, the Brenner’s measure, and the information–noise change measure. Then GAs are used to determine the “optimal” set of generalized transforms with the largest fitness function value. Based on the experimental results obtained in this study, the enhanced color images by the proposed approach are better than that by any of the three existing approaches for comparison. This shows the feasibility of the proposed approach.