Survey of an image contrast enhancement algorithm for grayscale images using particle swarm optimization

Image contrast enhancement is an important image processing technique for improving the visual quality and information content of images. We review this paper a novel contrast enhancement approach based on particle swarm optimization (PSO) and adaptive gamma correction. The method applies PSO to determine an optimal gamma value for gamma correction to adaptively enhance image contrast while preserving brightness. The optimization fitness function combines two objectives - maximizing image entropy to increase information content and edge content to preserve details. PSO searches for the gamma value that optimizes the fitness function. This technique is evaluated on low-contrast images and compared with state-of-the-art methods like histogram equalization, adaptive histogram equalization, contrast-limited adaptive histogram equalization, and fuzzy histogram equalization. Quantitative metrics and visual results demonstrate the proposed method's ability to effectively improve contrast while preserving naturalness and details. The algorithm shows promising performance for applications like medical imaging, satellite/aerial imaging, and night vision that require high contrast images without losing details. The key novelty lies in formulating contrast enhancement as a multi-objective optimization problem solved using PSO for automated and adaptive gamma correction.

Keywords:

Contrast enhancement , Entropy , Edge detection , PSO , Grayscale image