Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection. However, the quality of underwater images is often severely degraded due to the complexities of the underwater environment and equipment limitations. This degradation hinders advancements in relevant research. Consequently, underwater image restoration has gained significant attention as a research area. With the growing interest in deep-sea exploration, deep-sea image restoration has emerged as a new focus, presenting unique challenges. This paper aims to conduct a systematic review of underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. This paper first categorizes shallow-sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized, including color cast and blur caused by underwater environmental characteristics, as well as insufficient and uneven lighting caused by artificial light sources. Potential solutions are explored, such as applying general shallow-sea restoration methods to address color cast and blur, and leveraging techniques from related fields like exposure image correction and low-light image enhancement to tackle lighting issues. Comprehensive experiments are conducted to examine the feasibility of shallow-sea image restoration methods and related image enhancement techniques for deep-sea image restoration. The experimental results provide valuable insights into existing methods for addressing the challenges of deep-sea image restoration. An in-depth discussion is presented, suggesting several future development directions in deep-sea image restoration. Three main points emerged from the research findings: i) Existing shallow-sea image restoration methods are insufficient to address the degradation issues in deep-sea environments, such as low-light and uneven illumination. ii) Combining imaging physical models with deep learning to restore deep-sea image quality may potentially yield desirable results. iii) The application potential of unsupervised and zero-shot learning methods in deep-sea image restoration warrants further investigation, given their ability to work with limited training data.

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从浅海到深海：水下图像复原研究进展

水下图像在海洋工程、海洋开发、海洋环境保护等各个领域都发挥着至关重要的作用。然而，由于水下环境的复杂性和设备的限制，水下图像的质量往往会严重下降。这种退化阻碍了相关研究的进步。因此，水下图像恢复作为一个研究领域得到了极大的关注。随着人们对深海探索的兴趣日益浓厚，深海图像恢复成为新的焦点，提出了独特的挑战。本文旨在对水下图像复原技术进行系统综述，通过实验分析弥合浅海和深海图像复原领域的差距。本文首先将浅海图像复原方法分为三类：基于物理模型的方法、基于先验的方法和基于深度学习的融合物理模型的方法。分析了代表性方法的核心概念和特点。总结了深海图像复原的研究现状和主要挑战，包括水下环境特性导致的偏色和模糊，以及人工光源导致的光照不足和不均匀等问题。探索了潜在的解决方案，例如应用通用的浅海恢复方法来解决偏色和模糊问题，以及利用曝光图像校正和低光图像增强等相关领域的技术来解决照明问题。综合实验考察了浅海图像复原方法及相关图像增强技术用于深海图像复原的可行性。实验结果为解决深海图像恢复挑战的现有方法提供了宝贵的见解。提出了深入的讨论，提出了深海图像恢复的几个未来发展方向。研究发现了三个要点： i）现有的浅海图像恢复方法不足以解决深海环境中的退化问题，例如弱光和光照不均匀。ii) 将成像物理模型与深度学习相结合以恢复深海图像质量可能会产生理想的结果。