Semantically Contrastive Learning for Low-Light Image Enhancement
Keywords:Computer Vision (CV)
AbstractLow-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.
How to Cite
Liang, D., Li, L., Wei, M., Yang, S., Zhang, L., Yang, W., Du, Y., & Zhou, H. (2022). Semantically Contrastive Learning for Low-Light Image Enhancement. Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1555-1563. https://doi.org/10.1609/aaai.v36i2.20046
AAAI Technical Track on Computer Vision II