A Generalized Physical-knowledge-guided Dynamic Model for Underwater
  Image Enhancement
Pan Mu Hanning Xu Zheyuan Liu Zheng Wang Sixian Chan Cong Bai
Underwater images often suffer from color distortion and low contrast
resulting in various image types, due to the scattering and absorption of light
by water. While it is difficult to obtain high-quality paired training samples
with a generalized model. To tackle these challenges, we design a Generalized
Underwater image enhancement method via a Physical-knowledge-guided Dynamic
Model (short for GUPDM), consisting of three parts: Atmosphere-based Dynamic
Structure (ADS), Transmission-guided Dynamic Structure (TDS), and Prior-based
Multi-scale Structure (PMS). In particular, to cover complex underwater scenes,
this study changes the global atmosphere light and the transmission to simulate
various underwater image types (e.g., the underwater image color ranging from
yellow to blue) through the formation model. We then design ADS and TDS that
use dynamic convolutions to adaptively extract prior information from
underwater images and generate parameters for PMS. These two modules enable the
network to select appropriate parameters for various water types adaptively.
Besides, the multi-scale feature extraction module in PMS uses convolution
blocks with different kernel sizes and obtains weights for each feature map via
channel attention block and fuses them to boost the receptive field of the
network. The source code will be available at
\href{https://github.com/shiningZZ/GUPDM}{https://github.com/shiningZZ/GUPDM}.