Presentation of Robust Method in Image Contrast Enhancement Using Particle Swarm Optimization

Abstract-One of the most important processes in digital image processing is image contrast enhancement. Contrast enhancement may be itself a primary objective of the process or is performed as one of the pre-processing stages in order to obtain better quality for operating next major stages such as detection. In this thesis, multiobjective developed Particle Swarm Optimization (PSO) algorithm use to enhance gray digital image contrast so that image information content is maximized and also the mean intensity of the image is preserved as much as possible. The proposed method will be implemented on different images

Intensity-preserving contrast enhancement for gray-level images using multi-objective particle swarm optimization

This paper addresses the enhancement of the contrast of gray-level digital images while preserving the mean image intensity, thus, providing better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image with a continuous intensity transform function and the mean image intensity is preserved, by using the gamma-correction approach. Since the contrast enhancement and intensity preservation are contradicting, a multi-objective particle swarm optimization (MPSO) algorithm is developed to resolve this trade-off. Benchmark images, street senses and skyline images are included to illustrate the effectiveness of the proposed approach. © 2006 IEEE