Improved weighted thresholded histogram equalization algorithm for digital image contrast enhancement using the bat algorithm

Abstract

Contrast enhancement plays a fundamental role in image processing. Weighted thresholded histogram equalization (HE) is a well-known method for contrast enhancement, frequently used at the preprocessing stage in many image processing systems. Optimization of the weighting constraints is a hard optimization problem, and swarm intelligence metaheuristics have been successfully used for solving such problems. In this chapter we present an application of the bat algorithm (BA) to an image contrast enhancement problem. The proposed method improves performance of the weighted thresholded HE method by using the BA for optimizing weighting constrains. The performance of the proposed method was evaluated via quantitative and visual analysis. Discrete entropy of the image was used as an objective criterion for measuring image contrast enhancement. The experimental results show that, for the variety of test images, the proposed method enhances contrast effectively while preserving brightness and natural appearance.