  Due to the selective absorption and scattering of light by diverse aquatic
media, underwater images usually suffer from various visual degradations.
Existing underwater image enhancement (UIE) approaches that combine underwater
physical imaging models with neural networks often fail to accurately estimate
imaging model parameters such as depth and veiling light, resulting in poor
performance in certain scenarios. To address this issue, we propose a physical
model-guided framework for jointly training a Deep Degradation Model (DDM) with
any advanced UIE model. DDM includes three well-designed sub-networks to
accurately estimate various imaging parameters: a veiling light estimation
sub-network, a factors estimation sub-network, and a depth estimation
sub-network. Based on the estimated parameters and the underwater physical
imaging model, we impose physical constraints on the enhancement process by
modeling the relationship between underwater images and desired clean images,
i.e., outputs of the UIE model. Moreover, while our framework is compatible
with any UIE model, we design a simple yet effective fully convolutional UIE
model, termed UIEConv. UIEConv utilizes both global and local features for
image enhancement through a dual-branch structure. UIEConv trained within our
framework achieves remarkable enhancement results across diverse underwater
scenes. Furthermore, as a byproduct of UIE, the trained depth estimation
sub-network enables accurate underwater scene depth estimation. Extensive
experiments conducted in various real underwater imaging scenarios, including
deep-sea environments with artificial light sources, validate the effectiveness
of our framework and the UIEConv model.
