  Images taken under low-light conditions tend to suffer from poor visibility,
which can decrease image quality and even reduce the performance of the
downstream tasks. It is hard for a CNN-based method to learn generalized
features that can recover normal images from the ones under various unknow
low-light conditions. In this paper, we propose to incorporate the contrastive
learning into an illumination correction network to learn abstract
representations to distinguish various low-light conditions in the
representation space, with the purpose of enhancing the generalizability of the
network. Considering that light conditions can change the frequency components
of the images, the representations are learned and compared in both spatial and
frequency domains to make full advantage of the contrastive learning. The
proposed method is evaluated on LOL and LOL-V2 datasets, the results show that
the proposed method achieves better qualitative and quantitative results
compared with other state-of-the-arts.
