Swarm intelligent based contrast enhancement algorithm with improved visual perception for color images

Abstract

In this paper, we propose an optimized contrast enhancement algorithm for color images that improves visual perception of information. As color plays an important cue in many application areas, to prevent unwanted artifacts on color, our proposed method translates the color image into de-correlated lαβ color space based on the statistics of cone response to natural images. A color is defined in the lαβ space by an achromatic channel (brightness l), the red, green chrominance channel (α) and the yellow-blue chrominance channel (β). In order to avoid over saturation and annoying artifacts, our method is applied to the luminance component of the image and α and β are kept as constants. The key work of this paper is to use an adaptive gamma correction factor chosen by particle swarm optimization (PSO) to improve the entropy and enhance the details of the image. Gamma correction is a well-established technique that preserves the mean brightness of an image and produces more natural looking images by the choice of an optimal gamma factor. In the proposed method, the edge content and entropy are used as an objective function for each particle since a color image with good visual contrast includes many intensive edges. Since edges play a primary role in image understanding, one good way to enhance the contrast is to enhance the edges. Simulation results indicate that the proposed PSO optimized contrast enhancement improves overall image contrast and enriches the information present in the image. The proposed method is suitable for many real-time image processing applications.