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Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization. • A comprehensive review of underwater image enhancement and restoration methods is provided. • The publicly available underwater image datasets and underwater image quality metrics are introduced. • The extensive experiments on underwater image enhancement and restoration methods are conducted. • The challenges in the field are discussed and the promising insights for future directions are provided. [ABSTRACT FROM AUTHOR]

Underwater images often suffer from local distortions during the imaging and transmission process, which can negatively impact their quality. Fortunately, it is possible to improve image quality by removing local distortion without making any hardware or software adjustments to the transmission system. However, existing algorithms designed for global distortions are not suitable for addressing local distortions, while end-to-end restoration and inpainting algorithms do not perform satisfactorily on underwater images. To address this issue, this paper proposes a Joint distortion localization and restoration model based on Progressive Guidance (JPG) specifically tailored for underwater imaging and transmission. Our strategy employs a two-stage framework where the first stage focuses exclusively on accurately localizing distortions to obtain precise position. Subsequently, in the second stage, we utilize this position information for effective distortion restoration. To further enhance restoration performance, our approach progressively guides the restoration process by incorporating global, distortion-free as well as distortion-specific information into different components of the second-stage network. The work surpasses current state-of-the-art methods in restoring both mixed and individual distortions. • Establishing a dataset containing common local distortions in underwater images. • Proposing a distortion localization and restoration framework for underwater images. • Creating a network for extracting global, distortion-free, and distortion guidance. • Our method has proven competitive in restoring both single and mixed distortions. [ABSTRACT FROM AUTHOR]