Efficient Image Enhancement Using Beetle Antennae Search Algorithm

Abstract Image enhancement is a crucial process in various fields, including medical imaging, remote sensing, and digital photography. This study explores the application of the Beetle Antennae Search (BAS) algorithm for optimizing image enhancement parameters. The BAS algorithm is compared with the Particle Swarm Optimization (PSO) algorithm to evaluate its effectiveness in improving image quality. The objective is to enhance grayscale images by adjusting parameters such as A, B, C, and K to maximize a fitness function that incorporates edge information and entropy. Experimental results demonstrate that BAS consistently outperforms PSO in terms of convergence speed and image quality metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). BAS-enhanced images exhibit significant improvements in contrast, brightness, and sharpness, making it a promising tool for image enhancement tasks.