Enhanced Genetic Algorithm Applied for Image Denoising Problem

Abstract

This research introduces a new approach to restoring an image disrupted by salt and pepper (SPN) noise using a genetic algorithm (GA) at all densities, called Enhanced GA (EGA). The key contribution of the proposed algorithm is to merge the genetic algorithm with imaging approaches that are embedded into the population in order to achieve rapid convergence. The concept is to turn a group of entities into a variety of iterations using crossover and mutation operators. This method evolves a series of images rather than a series of filter parameters. Experimental simulation results on various images using a peak signal-to-noise ratio (PSNR), structural similarity index parameter (SSIM), demonstrate that the suggested algorithm outperforms other methods for eliminating SPN where the noise density is moderate and high. Keywords: Enhanced genetic algorithm, Genetic algorithm, Noise removal, Salt and pepper.