UW-CycleGAN: Model-Driven CycleGAN for Underwater Image Restoration

The formation of underwater images is a complex physical process that often suffers from various degradation factors, such as blurriness, low contrast, and color casts, which pose challenges for underwater object detection and recognition tasks. Because of the absence of reference images, learning-based methods that rely on unpaired images have been employed to enhance the underwater images. However, these methods may lose their effectiveness in real-world complex underwater environments. In this paper, we propose a model-driven cycle-consistent generative adversarial network (CycleGAN) model, which is inspired by the underwater image formation model to estimate the background light, transmission map, scene depth, and attenuation coefficient directly. Comprehensive experiments have demonstrated that our approach surpasses the compared underwater image restoration methods in both qualitative and quantitative aspects, providing restored images with satisfactory color saturation and brightness. We also conduct experiments on underwater object detection to illustrate the effectiveness of our CycleGAN in improving the detection accuracy. All our source codes and data are available at https://github.com/Duanlab123/UW-CycleGAN.