Impulse Noise Removal Based on Hybrid Genetic Algorithm

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In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.