Keywords : Filters


Hybrid Genetic Algorithm with Filters to Image Enhancement

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 107-119
DOI: 10.33899/csmj.2013.163550

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.
 

Using Genetic Algorithm to Reduce the Noise Effect on Images

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 127-142
DOI: 10.33899/csmj.2012.163723

This paper deals with a problem that concentrates on the noise removal that the images are affected from different resources employing Genetic Algorithm with filters. To achieve the aims of the paper, six types of genetic filters are suggested for noise removal from the images. These suggested genetic filters depending on filters (mean, median, min and max) as an objective function for them.
These suggested genetic filters are applied on several real images contaminated by two types of noise with different levels for comparison and to show the effectiveness of them. The result show that The fifth genetic filter that depends on the median filter as an objective function and heuristic crossover and  adding and subtracting mutation, gives the best results with RMSE=15.7243 and PSNR=24.1646 for Lena.bmp image and with RMSE=8.6197 and PSNR=29.4210 for girl.png image when add 0.05 salt & paper noise.