Gray level Image Enhancement by Improved Differential Evolution Algorithm

In this paper, an enhanced version of DE named MRLDE is used to solve the problem of image enhancement. The parameterized transformation function is used for image enhancement which uses both local and global information of image. For image enhancement, an objective criterion is considered which use the entropy and edge information of image. The objective of the DE is to maximize the objective fitness criterion in order to improve the contrast. Results of MRLDE are compared with basic DE, PSO, GA and with histogram equalization (HE) which is another popular enhancement technique. The obtained results indicate that proposed MRLDE yield better performance in the comparison of other techniques.