  Low-light image enhancement (LLIE) investigates how to improve illumination
and produce normal-light images. The majority of existing methods improve
low-light images via a global and uniform manner, without taking into account
the semantic information of different regions. Without semantic priors, a
network may easily deviate from a region's original color. To address this
issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that
can assist a low-light enhancement model in learning rich and diverse priors
encapsulated in a semantic segmentation model. We concentrate on incorporating
semantic knowledge from three key aspects: a semantic-aware embedding module
that wisely integrates semantic priors in feature representation space, a
semantic-guided color histogram loss that preserves color consistency of
various instances, and a semantic-guided adversarial loss that produces more
natural textures by semantic priors. Our SKF is appealing in acting as a
general framework in LLIE task. Extensive experiments show that models equipped
with the SKF significantly outperform the baselines on multiple datasets and
our SKF generalizes to different models and scenes well. The code is available
at Semantic-Aware-Low-Light-Image-Enhancement.
