A dynamically adapted and weighted Bat algorithm in image enhancement domain

Abstract

This paper proposed one improved Bat algorithm (BA) by incorporating one novel dynamic inertia weight and proposed self-adaptive strategies over algorithm’s parameters. Chaotic sequence and developed population diversity metric are employed over BA to perform the local search and generate one improved initial population respectively. The efficacy of the proposed BA is verified by applying it to set the parameters properly of the proposed histogram equalization (HE) variant; called weighted and thresholded Bi-HE (WTBHE). The proper setting of these parameters is time consuming but crucially effects WTBHE’s image enhancement ability. One novel co-occurrence matrix based objective function has been also formulated which facilitates the proposed BA for finding the optimal parameters of WBTHE which produces original brightness preserved enhanced images. Experimental results prove that the proposed BA is superior to simple BA in terms of convergence speed, robustness and maximization of objective function and WBTHE is better than some existing well-known HE variants in brightness preserving image enhancement field.