High-quality underwater optical images are essential for various applications of underwater vision. However, these images often suffer from severe degradation, complex noise, low contrast, and color cast, leading to poor image quality. To address these issues and accomplish related underwater vision tasks more smoothly, researchers have made many efforts to improve the quality of underwater optical images. This paper presents a comprehensive review of recent research, focusing on specific algorithms published between 2013 and 2023, while also predicting future research trends in this field. In light of the observed ambiguities and inconsistencies in previous review studies’ classification frameworks, this paper introduces a novel classification framework, grounded in the specific challenges in this domain, including issues unique to the imaging medium and imaging environment. Additionally, the proposed framework offers a more detailed categorization based on the algorithmic ideology of the authors. Furthermore, this paper addresses a significant gap in the literature by providing an in-depth assessment of the methods for removing marine snow noise from underwater optical images. By complementing existing research, this paper aims to enhance understanding of this subject and provide a clearer, more comprehensive, and in-depth exploration of underwater image quality improvement.

中文翻译：

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提高水下光学图像质量的算法：综合综述

高质量的水下光学图像对于水下视觉的各种应用至关重要。然而，这些图像经常出现严重退化、复杂噪声、低对比度和色偏等问题，导致图像质量较差。为了解决这些问题并更顺利地完成相关的水下视觉任务，研究人员在提高水下光学图像的质量方面做出了许多努力。本文对近期研究进行了全面回顾，重点关注 2013 年至 2023 年间发表的具体算法，同时也预测了该领域未来的研究趋势。鉴于先前综述研究的分类框架中观察到的模糊性和不一致之处，本文介绍了一种新颖的分类框架，该框架基于该领域的具体挑战，包括成像介质和成像环境特有的问题。此外，所提出的框架根据作者的算法思想提供了更详细的分类。此外，本文通过对从水下光学图像中去除海洋雪噪声的方法进行深入评估，解决了文献中的一个重大空白。通过补充现有的研究，本文旨在增强对该主题的理解，并对水下图像质量改善提供更清晰、更全面、更深入的探索。