Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

Image enhancement is an essential step in image analysis and processing as it helps people to recognize and understand images because their perception is greatly influenced by image quality. Incomplete beta function (IBF) is a broadly employed transformation function for image contrast enhancement (ICE). However, IBF has low parameter selection efficiency, a limited range of mutable parameters to stretch areas with high or low gray levels, and image enhancement with stretching at both ends is almost ineffectual. In this paper, a hybrid whale optimization algorithm (WOA) with the Chameleon Swarm algorithm (CSA), referred to as HWOA, is presented to adaptively determine the optimal parameters of IBF for ICE. Then, bilateral gamma correction (BGC) is utilized to produce better contrast and brightness while preserving edge detail. The proposed HWOA algorithm follows a multi-phased process of strategies. Many improvements were made to the mathematical model of WOA followed by its hybridization with CSA for further exploration and exploitation aspects. The proposed HWOA is tested over some standard images along with well-known available Kodak image dataset and assessed using several standard measures. The experimental results showed that the proposed algorithm can satisfactorily surpass many other algorithms that used the same image enhancement approach as well as other traditional image enhancement methods deemed here for comparison. Specifically, the findings on ten color images revealed that the performance of HWOA in terms of average peak signal-to-noise ratio, average structural similarity index, and average values of entropy results are more than 33.0, 96.6%, and 7.3, respectively, and these results are much better than all other comparative methods in the corresponding evaluation criteria.