An Evolutionary Approach to Image Noise Cancellation Using Adaptive Particle Swarm Optimization (APSO)

In this paper, we propose a novel method which is an effective implementation of Population Particle Swarm Optimization aiming at optimizing the noise removal process in the case of grayscale images contaminated with salt and pepper noise. A new neighborhood average filter has been used in conjunction with APSO for noise removal. Simulations reveal that the proposed scheme which has been designed specifically for noise removal works well in suppressing noise impulses in images corrupted with different levels of noise. The results of the proposed algorithm are compared with those obtained by PSO-CNN method for gray-scale image noise cancellation.