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}.

翻译：水下图像常因水体对光的散射与吸收而出现色彩失真和对比度低等问题，导致图像类型多样化。同时，获取高质量配对训练样本以实现通用化建模颇具挑战。针对这些难点，我们提出一种基于物理知识引导的动态模型的水下图像增强通用方法（简称GUPDM），该方法由三部分组成：基于大气光的动态结构（ADS）、基于透射率的动态结构（TDS）和基于先验的多尺度结构（PMS）。具体而言，为覆盖复杂水下场景，本研究通过成像模型改变全局大气光与透射率，以模拟不同水下图像类型（如从黄色到蓝色的水下图像色彩变化）。随后设计ADS和TDS模块，利用动态卷积从水下图像中自适应提取先验信息，并为PMS生成参数。这两个模块使网络能针对不同水体类型自适应选择合适参数。此外，PMS中的多尺度特征提取模块采用不同核大小的卷积块，通过通道注意力模块获取各特征图权重并融合，以扩大网络感受野。源代码将发布于\href{https://github.com/shiningZZ/GUPDM}{https://github.com/shiningZZ/GUPDM}。