Toward underwater image enhancement: new dataset and white balance priors-based fusion network.

Raw underwater images are degraded due to light attenuation and particle scattering. It is challenging to obtain authentic reference images. Hence, existing underwater datasets may be short of paired and trainable images. Many data-driven approaches are therefore difficult to implement. To address the above issues, we mainly make two contributions. First, a paired artificial underwater dataset (i.e., PAU-Wild) is constructed, including more than 3500 pairs of images of seven types. PAU-Wild is the first attempt to simulate real-world underwater images with external filters. Second, to enhance underwater images, a convolutional neural network using multistage fusion is proposed. Our model introduces two white balance (WB) priors into the fusion strategy. In addition, a joint loss function is designed to preserve content features and edge features. The experimental results demonstrate that our method is effective and even outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]