DuGAN: An effective framework for underwater image enhancement

Abstract

Underwater image enhancement is an important low-level vision task with much attention of community. Clear underwater images are helpful for underwater operations. However, raw underwater images often suffer from different types of distortions caused by the underwater environment. To solve these problems, this paper proposes an end-to-end dual generative adversarial network (DuGAN) for underwater image enhancement. The images processed by existing methods are taken as training samples for reference, and they are segmented into clear parts and unclear parts. Two discriminators are used to complete adversarial training toward different areas of images with different training strategies, respectively. The proposed method is able to output more pleasing images than reference images benefit by this framework. Meanwhile, to ensure the authenticity of the enhanced images, content loss, adversarial loss, and style loss are combined as loss function of our framework. This framework is easy to use, and the subjective and objective experiments show that excellent results are achieved compared to those methods mentioned in the literature.