Underwater image enhancement (UIE) is vital for high-level vision-related underwater tasks. Although learning-based UIE methods have made remarkable achievements in recent years, it's still challenging for them to consistently deal with various underwater conditions, which could be caused by: 1) the use of the simplified atmospheric image formation model in UIE may result in severe errors; 2) the network trained solely with synthetic images might have difficulty in generalizing well to real underwater images. In this work, we, for the first time, propose a framework \textit{SyreaNet} for UIE that integrates both synthetic and real data under the guidance of the revised underwater image formation model and novel domain adaptation (DA) strategies. First, an underwater image synthesis module based on the revised model is proposed. Then, a physically guided disentangled network is designed to predict the clear images by combining both synthetic and real underwater images. The intra- and inter-domain gaps are abridged by fully exchanging the domain knowledge. Extensive experiments demonstrate the superiority of our framework over other state-of-the-art (SOTA) learning-based UIE methods qualitatively and quantitatively. The code and dataset are publicly available at https://github.com/RockWenJJ/SyreaNet.git.

翻译：水下图像增强对于高级视觉相关的水下任务至关重要。尽管近年来基于学习的水下图像增强方法取得了显著进展，但它们在应对各种水下条件时仍面临挑战，这可能是由以下原因造成的：1）水下图像增强中使用的简化大气成像模型可能导致严重误差；2）仅使用合成图像训练的网络可能难以很好地泛化到真实水下图像。在本工作中，我们首次提出了一个名为SyreaNet的水下图像增强框架，该框架在修订后的水下图像形成模型和新型域适应策略的指导下，整合了合成和真实数据。首先，基于修订模型提出了水下图像合成模块。然后，设计了一个物理引导的解耦网络，通过结合合成和真实水下图像来预测清晰图像。通过充分交换域知识，缩小了域内和域间差距。大量实验定性和定量地证明了我们框架相对于其他最先进的基于学习的水下图像增强方法的优越性。代码和数据集已在https://github.com/RockWenJJ/SyreaNet.git上公开。