Image Enhancement using Gray Wolf and Whale optimization algorithms

Image enhancement is one of the preprocessing phases in image processing. Image enhancement can effectively improve the appearance of the image for the realization purpose so that the image can be utilized for analytics and the human visual system. The central aim of image enhancement is to induce detail that is concealed in an image or to escalate contrast in a low-contrast image. The contrast enhancement aims to mend the quality of an image to turn it into a more appropriate one for a specific presentation. Till today, many image enhancement methods have been suggested for several presentations and lots of attempts have been made to find a way to further intensify the quality of the enhancement results and reduce the computational intricacy and memory usage. In this paper, image contrast enhancement has been regarded as an optimization problem and a parameterized transformation function, which contains the local and global information of the image was applied to increase the contrast of an image. Two meta-heuristic algorithms i.e. Whale Optimization Algorithm (WOA) and Grey Wolf Optimization algorithm (GWO) were utilized to find the optimal amount of the parameters while maximizing the objective function. The objective function was made up of entropy, edge information of the image, and the structural similarity index measure (SSIM). The outcomes of the suggested method were matched with PSO and with two classic enhancement techniques, including histogram equalization, and contrast stretching. The experimental findings demonstrate that the suggested method outperforms the others.

Keywords:

Image enhancement , Particle swarm optimization , Whale optimization algorithm , Histogram equalization , Contrast stretching.