Perception-Driven Deep Underwater Image Enhancementusing Retinex-Guided Deep Learning

Underwater images often suffer from severe quality degradation due to wavelength-dependent light absorption, scattering, and non-uniform illumination conditions present in aquatic environments. These effects lead to low contrast, color distortion, poor visibility, and loss of fine details, which significantly impact downstream applications such as marine exploration, underwater robotics, and visual inspection. This paper presents a perception-driven deep underwater image enhancement approach that integrates Retinex-based illumination modeling with a Generative Adversarial Network (GAN). The proposed method estimates illumination maps to guide the enhancement process, enabling effective separation of illumination and reflectance components. A GAN framework is employed to learn perceptually enhanced illumination correction while preserving structural details and natural color appearance. Post-processing techniques such as gamma correction and edgepreserving refinement are applied to further improve visual quality. Experimental results demonstrate improved brightness, contrast, and color balance compared to raw underwater images. Quantitative evaluation using PSNR, SSIM, UIQM, and UCIQE metrics confirms the effectiveness of the proposed method in enhancing underwater imagery while maintaining perceptual realism.