  Low-light image enhancement (LLE) remains challenging due to the unfavorable
prevailing low-contrast and weak-visibility problems of single RGB images. In
this paper, we respond to the intriguing learning-related question -- if
leveraging both accessible unpaired over/underexposed images and high-level
semantic guidance, can improve the performance of cutting-edge LLE models?
Here, we propose an effective semantically contrastive learning paradigm for
LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image
enhancement task as multi-task joint learning, where LLE is converted into
three constraints of contrastive learning, semantic brightness consistency, and
feature preservation for simultaneously ensuring the exposure, texture, and
color consistency. SCL-LLE allows the LLE model to learn from unpaired
positives (normal-light)/negatives (over/underexposed), and enables it to
interact with the scene semantics to regularize the image enhancement network,
yet the interaction of high-level semantic knowledge and the low-level signal
prior is seldom investigated in previous methods. Training on readily available
open data, extensive experiments demonstrate that our method surpasses the
state-of-the-arts LLE models over six independent cross-scenes datasets.
Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation
under extremely dark conditions is discussed. Source Code:
https://github.com/LingLIx/SCL-LLE.
