  Recently, learning-based algorithms have shown impressive performance in
underwater image enhancement. Most of them resort to training on synthetic data
and achieve outstanding performance. However, these methods ignore the
significant domain gap between the synthetic and real data (i.e., interdomain
gap), and thus the models trained on synthetic data often fail to generalize
well to real underwater scenarios. Furthermore, the complex and changeable
underwater environment also causes a great distribution gap among the real data
itself (i.e., intra-domain gap). However, almost no research focuses on this
problem and thus their techniques often produce visually unpleasing artifacts
and color distortions on various real images. Motivated by these observations,
we propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to
simultaneously minimize the inter-domain and intra-domain gap. Concretely, a
new dual-alignment network is designed in the first phase, including a
translation part for enhancing realism of input images, followed by an
enhancement part. With performing image-level and feature-level adaptation in
two parts by jointly adversarial learning, the network can better build
invariance across domains and thus bridge the inter-domain gap. In the second
phase, we perform an easy-hard classification of real data according to the
assessed quality of enhanced images, where a rank-based underwater quality
assessment method is embedded. By leveraging implicit quality information
learned from rankings, this method can more accurately assess the perceptual
quality of enhanced images. Using pseudo labels from the easy part, an
easy-hard adaptation technique is then conducted to effectively decrease the
intra-domain gap between easy and hard samples.
