Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Abstract

Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H-DIBCO 2016 and H-DIBCO 2018 datasets. The source code of this work is available at https://github.com/ahmed-shameem/Projects.

Keywords

• Barnacle Mating Optimizer
• image contrast enhancement
• meta-heuristic
• evolutionary algorithm
• DIBCO