The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and (2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.

We first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented, including architecture, parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.

深度学习的强大表示能力已使水下图像增强社区不可避免地发挥其潜力。随着时间的流逝，对深层水下图像增强网络的探索正在增加。因此，需要一个小时进行全面调查。在本文中，我们的主要目标是双重的（1）：对基于深度学习的水下图像增强功能进行全面而深入的调查，涵盖从算法到开放性问题的各种观点，以及（2） ：对各种数据集上的深层算法进行定性和定量比较，以此作为基准，而之前从未进行过探索。

我们首先介绍水下图像形成模型，该模型是训练数据综合和深层网络设计的基础，也有助于理解水下图像退化的过程。然后，我们回顾了深层水下图像增强算法，并对当前网络的某些方面进行了简要介绍，包括体系结构，参数，训练数据，损失函数和训练配置。我们还总结了评估指标和水下图像数据集。随后，进行了系统的实验比较，以分析深度算法的鲁棒性和有效性。同时，我们指出了当前基准数据集和评估指标的缺点。最后，我们讨论了几个未解决的未解决问题，并提出了可能的研究方向。