Unsupervised and Untrained Underwater Image Restoration Based on Physical Image Formation Model

Shu Chai, Zhenqi Fu, Yue Huang, Xiaotong Tu, Xinghao Ding

Abstract: Underwater images suffer from degradation caused by light scattering and absorption. Training a deep neural network to restore underwater images is challenging due to the labor-intensive data collection and the lack of paired data. To this end, we propose an unsupervised and untrained underwater image restoration method based on the layer disentanglement and the underwater image formation model. Specifically, our network disentangles an underwater image into four components, i.e., the scene radiance, the direct transmission map, the backscatter transmission map, and the global background light, which are further combined to reconstruct the underwater image in a self-supervised manner. Our method can avoid using paired training data and large-scale datasets, benefiting from the unsupervised and untrained characteristics. Extensive experiments demonstrated that our method obtains promising performance compared with six methods on three real-world underwater image databases.