Image Contrast Enhancement: The Synergistic Power of a Dual-Gamma Correction Function and Evolutionary Algorithms

Abstract

Image processing techniques frequently employ contrast enhancement to emphasize lighting, brightness, and finer details, facilitating better visualization and rendering of images suitable forvarious applications and research endeavors. The effectiveness of these techniques significantly influences task performance in fields reliant on image analysis, with disciplines like computer vision particularly relianton precise feature detection. This paper introduces an innovative approach to contrast enhancement using a dual-gamma correction function, their parameters optimized through metaheuristics. By examining the interplay between the dual-gamma correction function and prominent metaheuristic algorithms suchas a Genetic Algorithm (GA), a Differential Evolution (DE) algorithm, and a Particle Swarm Optimization (PSO) algorithm, the goal is to refine pixel values and accentuate features in low-contrast images. The methodis subjected to a comprehensive evaluation using a publicly available dataset and established performance metrics, ensuring the reliability and validity of the results. The results of this study are promising and have significant practical implications. The dual-gamma correction function enhances image contrast and adapts swiftly to various metaheuristics. This research is acrucial step towards advancing contrast enhancement methodologies, offering a practical and effective solution for improving image quality across multiple applications.