An Analysis of Genetic Algorithm with Training of Image Filter Kernel Matrix

Abstract

In the presented study, training of kernel matrix which is used for convolution or correlation image filtering operations using genetic algorithms was analyzed. For this purpose, filtering performance was analyzed according to the size of kernel matrix and the size of the image. In the experimental studies, various sizes of images and various example images for each size were used. Noisy images were formed by adding Gaussian noise. Computations were obtained for fixed number of population and iterations to see the effect of parameters. According to obtained results, the effect of image size variation is at small levels when the same filtering kernel is used. Performance degradation was observed as the size of the kernel matrix is increased. This degradation which is due to the increase in the number of variables of genetic algorithm is improved by increasing the number of iteration.