  Underwater image enhancement has become an attractive topic as a significant
technology in marine engineering and aquatic robotics. However, the limited
number of datasets and imperfect hand-crafted ground truth weaken its
robustness to unseen scenarios, and hamper the application to high-level vision
tasks. To address the above limitations, we develop an efficient and compact
enhancement network in collaboration with a high-level semantic-aware
pretrained model, aiming to exploit its hierarchical feature representation as
an auxiliary for the low-level underwater image enhancement. Specifically, we
tend to characterize the shallow layer features as textures while the deep
layer features as structures in the semantic-aware model, and propose a
multi-path Contextual Feature Refinement Module (CFRM) to refine features in
multiple scales and model the correlation between different features. In
addition, a feature dominative network is devised to perform channel-wise
modulation on the aggregated texture and structure features for the adaptation
to different feature patterns of the enhancement network. Extensive experiments
on benchmarks demonstrate that the proposed algorithm achieves more appealing
results and outperforms state-of-the-art methods by large margins. We also
apply the proposed algorithm to the underwater salient object detection task to
reveal the favorable semantic-aware ability for high-level vision tasks. The
code is available at STSC.
