Conditional generative adversarial network with dual-branch progressive generator for underwater image enhancement

Peng Lin, Yafei Wang, Guangyuan Wang, Xiaohong Yan, Guangqi Jiang, Xianping Fu

Abstract: Underwater images are of great significance for exploring and utilizing the marine environment. However, the raw underwater images usually suffer from distorted color and low contrast due to the attenuation of light. To solve this problem, we present a conditional generative adversarial network with dual-branch progressive generator, which can asymptotically enhance underwater images. In particular, the generator consists of two independent branches and a progressive enhancement algorithm. The dual-branch structure is designed to generate a base image and numerous parameter maps required by progressive enhancement respectively. The progressive enhancement algorithm is proposed to iteratively improve the quality of underwater images. Meanwhile, an iterative function is constructed to guide image enhancement in the progressive enhancement algorithm. Finally, a simple discriminator and multiple effective loss functions are adopted to optimize the progressive process of underwater image enhancement. The qualitative and quantitative experiments on synthetic and real-world underwater datasets demonstrate that the proposed method can achieve superior performance against several representative underwater image processing methods. Furthermore, a series of ablation studies are presented to show the contribution of each branch in our model.