Enhanced Underwater Image Dehazing Using Dark Channel Prior: A Comparative Analysis of Transmission Map Estimation Methods

Underwater images often suffer from poor visibility due to light scattering and absorption, leading to reduced contrast, color distortion, and hazy appearances. The Dark Channel Prior (DCP) technique, which estimates the transmission map and helps restore sharp and aesthetically pleasing pictures, is frequently used for image dehazing in order to solve this problem. Nonetheless, precise assessment of the transmission map is essential for efficient dehazing. Four distinct transmission map estimate techniques within the DCP framework are compared in this paper: approaches that are based on (i) average intensity, (ii) entropy, (iii) for-loop, and (iv) normal omega.Standard image quality criteria, such as the Natural Image Quality Evaluator (NIQE), Entropy, Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), are used to assess how well these techniques work. According to experimental data, the highest performance is obtained with the Entropy-based transmission estimation approach, which improves perceptual quality, structural similarity, and entropy in underwater pictures. The efficiency of entropy-based transmission estimation and its potential for real-time underwater picture enhancing applications are demonstrated in this work.