The underwater images are captured within diverse water-medium conditions, leading to complex degradation, including color bias, low contrast, and blur effect. Recently, learning-based methods have demonstrated their potential for underwater image enhancement (UIE). However, most of the previous work focus on the training strategy or network design to make the enhanced result aligned well with the labels in datasets, ignoring that the labels are selected from the enhanced results of previous UIE methods and these pseudo-labels are noisy. Consequently, the performance of their models is not satisfactory to a certain extent. However, collecting the true labels of the underwater images is challenging. In this work, we propose a transfer learning-based UIE that does not require underwater images to have paired noisy or true labels for learning. Instead, the UIE task is first divided into global color correction, haze removal, and background noise suppression following the underwater physics. Then multiple types of prior from other vision tasks are leveraged as cross-domain supervision in each step. In this way, a novel UIE is available via transfer learning, and the physics-aligned UIE decomposition provides theoretical soundness. Qualitative and quantitative experiments demonstrate that our proposal based on physics and priors fusion achieves SOTA performance in the UIE task and effectively boosts downstream vision tasks, significantly outperforming benchmark methods. Project repo: https://github.com/Haru2022/P2-UIE.

翻译：水下图像在多种水质条件下拍摄，导致出现色彩偏差、低对比度和模糊效应等复杂退化现象。近年来，基于学习的方法在水下图像增强（UIE）领域展现出潜力。然而，多数现有工作聚焦于训练策略或网络设计，使增强结果与数据集中的标签对齐，却忽略了这些标签选自先前UIE方法的增强结果且为含噪伪标签的事实。因此，这些模型的性能在一定程度上不尽人意。但收集水下图像的真实标签极具挑战性。本文提出一种基于迁移学习的UIE方法，无需配对含噪或真实标签的水下图像进行学习。首先，依据水下物理原理将UIE任务分解为全局颜色校正、去雾和背景噪声抑制三个子任务。继而，将来自其他视觉任务的多种先验信息作为跨域监督应用于各步骤中。通过这种方式，基于迁移学习的新型UIE方法得以实现，且符合物理规律的UIE分解过程提供了理论依据。定性与定量实验表明，基于物理与先验融合的本文方法在UIE任务中达到最优性能，并有效提升下游视觉任务效果，显著超越基准方法。项目仓库：https://github.com/Haru2022/P2-UIE。