Image Restoration Using Improved Particle Swarm Optimization

Abstract

Aiming at too many restrictions in conventional image restoration methods, an image restoration method based on improved particle swarm optimization is proposed. This thesis introduces a selection process of genetic algorithm into standard particle swarm optimization, which resolves the problem of premature convergence of the standard particle swarm optimization parameters in image restoration. In this paper, the algorithm converts the gray image restoration problem to genetic algorithm optimize problem, and it is applied to improve image restoration and processing speed. Finally, experimental results are presented to validate the efficiency of the proposed scheme, further, its performance is compared with other conventional image restoration methods.