Physics-Aware Semi-Supervised Underwater Image Enhancement
Hao Qi Xinghui Dong
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.