Hybrid genetic algorithm with filters to image enhancement

Abstract EN

Image enhancement is a useful and necessary part of image processing and its analysis.

The quality of an image could be corrupted by different kinds of noises, added due to the undesired conditions or during the transmission.

In this paper, a Hybrid Genetic Algorithm with Filters (HGAF) is suggested for the removing of impulse noise from digital images.

The new suggested algorithm HGAF uses popular (mean, median and min-max filters) and other proposed filters as fitness function for it in order to design eight proposed genetic filters.

These eight proposed genetic filters are applied on several gray images corrupted by two types of noise (salt-and-pepper and gaussian noises) with different levels for comparison and to show the effectiveness of them by using the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).

Also, proposed two methods of parents selection to compare between them and types of crossovers and mutations that are used.