A particle swarm optimization method for tuning the parameters of multiscale retinex based color image enhancement

In this paper, a Particle Swarm Optimization (PSO) method for tuning the parameters of multiscale retinex based color image enhancement is presented. The image enhancement using multiscale retinex scheme heavily depends on parameters such as Gaussian surround space constants, number of scales, gain and offset etc. Due to hard selection of these parameters, PSO has been used in order to investigate the optimal parameters for the best image enhancement. The PSO method of parameter tuning adopted for multiscale retinex with modified color restoration (MSRMCR) algorithm achieves very good quality of reconstructed images, far better than that possible with the other existing methods. The presented algorithm is compared with other promising enhancement schemes such as histogram equalization, NASA's multiscale retinex with color restoration (MSRCR), Improved MSRCR (IMSRCR), and Photoflair software. The quality of the enhanced image is validated iteratively using an efficient objective criterion which is based on entropy and edge information of an image. Finally, the quality of the reconstructed images obtained by the proposed method is evaluated using Wavelet Energy (WE) metric. The experimental results presented shows that color image enhanced by the proposed algorithm are clearer, vivid and efficient.