Semantically Contrastive Learning for Low-light Image Enhancement

Low-light image enhancement (LLE) remains challenging

due to the unfavorable prevailing low-contrast and weakvisibility 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 lowlevel signal prior is seldom investigated in previous methods. Training on readily available open data, extensive experiments demonstrate that our method surpasses the state-of-thearts LLE models over six independent cross-scenes datasets.

Moreover, SCL-LLE’s potential to beneft the downstream semantic segmentation under extremely dark conditions is discussed. Source Code: https://github.com/LingLIx/SCL-LLE.