Underwater image enhancement (UIE) is a challenging research task in the
field of computer vision. Although hundreds of UIE algorithms have been
proposed, a comprehensive and systematic review is still lacking. To promote
future research, we summarize the UIE task from multiple perspectives. First,
the physical models, data construction processes, evaluation metrics, and loss
functions are introduced. Second, according to the contributions brought by
different literatures, recent proposed algorithms are discussed and classified
from six perspectives, namely network architecture, learning strategy, learning
stage, assistance task, domain perspective and disentanglement fusion,
respectively. Third, considering the inconsistencies in experimental settings
in different literatures, a comprehensive and fair comparison does not yet
exist. To this end, we quantitatively and qualitatively evaluate
state-of-the-art algorithms on multiple benchmark datasets. Finally, issues
worthy of further research in the UIE task are raised. A collection of useful
materials is available at this https URL

水下图像增强是计算机视觉领域的一个具有挑战性的研究任务，文章综述了多个角度，包括物理模型、数据构建过程、评估指标、损失函数以及最新算法在网络架构、学习策略、学习阶段、辅助任务、领域视角和解缠融合等六个方面的贡献，并通过定量和定性评估在多个基准数据集上对最先进的算法进行了综合和公正的比较，同时提出了水下图像增强任务中值得进一步研究的问题。