Two-Stage Underwater Image Restoration Algorithm Based on Physical Model and Causal Intervention

Underwater images often suffer from severe color degradation, haze and local blur, which are caused by the scattering and absorption effects of light in water. Firstly, to address the lack of annotations in underwater images, we propose a multi-degradation rate underwater image degradation model. Additionally, we use the generative adversarial network (GAN) to guide the underwater image degradation model and generate paired images that can be used for underwater image restoration (UIR) network training. Also, given the problems of poor restoration quality, serious loss of detail, and slow inference speed of existing deep learning algorithms, we construct an underwater image restoration network that can reference in real-time. Moreover, through causal interventions in the image generation process, spurious correlations between global features and detailed features are eliminated. As a result, the detail generation ability of the image is improved. Experiments on real underwater image datasets demonstrate that compared with existing methods, our proposed method more effectively solves the problem of underwater image degradation.