  Due to the wavelength-dependent light attenuation, refraction and scattering,
underwater images usually suffer from color distortion and blurred details.
However, due to the limited number of paired underwater images with undistorted
images as reference, training deep enhancement models for diverse degradation
types is quite difficult. To boost the performance of data-driven approaches,
it is essential to establish more effective learning mechanisms that mine
richer supervised information from limited training sample resources. In this
paper, we propose a novel underwater image enhancement network, called
SGUIE-Net, in which we introduce semantic information as high-level guidance
across different images that share common semantic regions. Accordingly, we
propose semantic region-wise enhancement module to perceive the degradation of
different semantic regions from multiple scales and feed it back to the global
attention features extracted from its original scale. This strategy helps to
achieve robust and visually pleasant enhancements to different semantic
objects, which should thanks to the guidance of semantic information for
differentiated enhancement. More importantly, for those degradation types that
are not common in the training sample distribution, the guidance connects them
with the already well-learned types according to their semantic relevance.
Extensive experiments on the publicly available datasets and our proposed
dataset demonstrated the impressive performance of SGUIE-Net. The code and
proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html
