  Underwater image restoration attracts significant attention due to its
importance in unveiling the underwater world. This paper elaborates on a novel
method that achieves state-of-the-art results for underwater image restoration
based on the unsupervised image-to-image translation framework. We design our
method by leveraging from contrastive learning and generative adversarial
networks to maximize mutual information between raw and restored images.
Additionally, we release a large-scale real underwater image dataset to support
both paired and unpaired training modules. Extensive experiments with
comparisons to recent approaches further demonstrate the superiority of our
proposed method.
