Seeing Through the Haze: A Comprehensive Review of Underwater Image Enhancement Techniques

Abstract

Underwater imaging suffers from significant quality degradation due to light scattering and absorption by water molecules, leading to color cast and reduced visibility. This hinders the ability to analyze and interpret the underwater world. Image dehazing techniques have emerged as a crucial component for underwater image enhancement (UIE). This review comprehensively examines both traditional methods, rooted in the physics of light transmission in water, and recent advances in learning-based approaches, particularly deep learning architectures like Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformers. We conduct a comparative analysis across various metrics, including visual quality, color fidelity, robustness to noise, and computational efficiency, to highlight the strengths and weaknesses of each approach. Furthermore, we address key challenges and future directions for traditional and learning-based methods, focusing on domain adaptation, real-time processing, and integrating physical priors into deep learning models. This review provides valuable insights and recommendations for researchers and practitioners in underwater image enhancement.