Underwater image restoration is of significant importance in unveiling the
underwater world. Numerous techniques and algorithms have been developed in the
past decades. However, due to fundamental difficulties associated with
imaging/sensing, lighting, and refractive geometric distortions, in capturing
clear underwater images, no comprehensive evaluations have been conducted of
underwater image restoration. To address this gap, we have constructed a
large-scale real underwater image dataset, dubbed `HICRD' (Heron Island Coral
Reef Dataset), for the purpose of benchmarking existing methods and supporting
the development of new deep-learning based methods. We employ accurate water
parameter (diffuse attenuation coefficient) in generating reference images.
There are 2000 reference restored images and 6003 original underwater images in
the unpaired training set. Further, we present a novel method for underwater
image restoration based on unsupervised image-to-image translation framework.
Our proposed method leveraged contrastive learning and generative adversarial
networks to maximize the mutual information between raw and restored images.
Extensive experiments with comparisons to recent approaches further demonstrate
the superiority of our proposed method. Our code and dataset are publicly
available at GitHub.

通过构建大规模的 HICRD 数据集并提出新的基于 unsupervised image-to-image translation 框架、利用对比学习和生成式对抗网络的方法，我们的研究试图弥补基础难题导致的现有技术和算法难以对海底原始图片修复进行全面评估的空白，并在与其他方法的比较中证明了我们的方法的卓越性能。