Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been used to address this problem. However, the inflexible assumption of the former often impairs their effectiveness in handling diverse underwater scenes, while the generalization of the latter to unseen images is usually weakened by insufficient data. In this study, we leverage both the physics-based underwater Image Formation Model (IFM) and deep learning techniques for Underwater Image Enhancement (UIE). To this end, we propose a novel Physics-Aware Dual-Stream Underwater Image Enhancement Network, i.e., PA-UIENet, which comprises a Transmission Estimation Steam (T-Stream) and an Ambient Light Estimation Stream (A-Stream). This network fulfills the UIE task by explicitly estimating the degradation parameters of the IFM. We also adopt an IFM-inspired semi-supervised learning framework, which exploits both the labeled and unlabeled images, to address the issue of insufficient data. Our method performs better than, or at least comparably to, eight baselines across five testing sets in the degradation estimation and UIE tasks. This should be due to the fact that it not only can model the degradation but also can learn the characteristics of diverse underwater scenes.

翻译：水下图像通常因水体的传输介质而遭受退化。传统基于先验的方法和基于深度学习的方法都已被用于解决这一问题。然而，前者的刚性假设往往损害其在处理多样水下场景时的有效性，而后者对未见图像的泛化能力通常因数据不足而被削弱。在本研究中，我们同时利用基于物理的水下图像形成模型（IFM）和深度学习技术进行水下图像增强（UIE）。为此，我们提出了一种新颖的基于物理信息的双流水下图像增强网络，即PA-UIENet，它包含一个传输估计流（T-Stream）和一个环境光估计流（A-Stream）。该网络通过显式估计IFM的退化参数来完成UIE任务。我们还采用了一种受IFM启发的半监督学习框架，该框架同时利用有标签和无标签图像，以解决数据不足的问题。在五个测试集上的退化估计和UIE任务中，我们的方法表现优于或至少与八种基线方法相当。这应归因于该方法不仅能对退化进行建模，还能学习多样水下场景的特性。