Single Underwater Image Restoration via Unsupervised Generative Adversarial Network and Contrastive Learning

Underwater image restoration has gained more and more attention recently due to its several applications in marine environmental surveillance-related tasks. In this paper, a novel unsupervised GAN (generative adversarial network)-based deep learning framework for single underwater image restoration is proposed. Without needing paired training images, we introduce contrastive learning with feature and style reconstruction loss functions in our unsupervised GAN-based structure to learn an image generator for translating underwater images to the corresponding in-air images. Extensive experiments have shown that the proposed method outperforms (or is comparable with) the state-of-the-art deep learning-based methods relying on paired/unpaired training data quantitatively and qualitatively.