  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.
