Recent Advances in Underwater Image Restoration and Enhancement

Abstract

The objective of this paper is to conduct an organized overview of the literature on underwater image processing, present a comprehensive taxonomy, and provide a critical evaluation of image restoration and enhancement techniques. This study classified underwater image processing methods into physical-modelbased image restoration and non-physical-model image enhancement The study notes that deep learning has become increasingly important in underwater image processing. A notable phenomenon is that more effort is being devoted to integrating physical knowledge with learning networks, thereby simultaneously enhancing their generalization ability while preserving the real-world scene. In this paper, the authors discuss classification methods and analyze their advantages, limitations, and deployment performance. Research on underwater image processing is moving toward more integrated, robust, and efficient solutions. Future studies should focus on balancing performance and computational complexity, developing perceptual quality metrics, and investigating self-supervised learning paradigms. This survey provides researchers with a comprehensive overview and a useful reference point for understanding this field.