A Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm for Image Contrast Enhancement

Abstract

Image contrast enhancement is a vital part of image processing application for improving visual and informational quality of a distorted image. For this purpose, Conventional Histogram Equalization techniques are most common approaches for both the purpose of enhancing the image contrast and preserving its main characteristics. But conventional HE techniques are not suitable all the times for preserving all the image characteristics to improve the overall quality of an image. In this regard, optimization techniques provide better results by controlling proper parameters for different methods. This paper shows the implementation of a hybrid optimization technique comprising of the search dynamics of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The effective output from PSO search algorithm has been implemented with the ABC techniques to get better contrast enhancement while optimizing the objective function designed towards preserving the important characteristics of the low contrast images. The method is tested with different test images. The output is compared with the conventional techniques in both visually and against different image quality metrics. The visual results as well as the metric-based comparisons show the potential of the presented method over the conventional techniques.