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.

翻译：在低光照条件下拍摄的图像往往存在可见度差的问题，这会降低图像质量，甚至削弱下游任务的性能。基于CNN的方法难以学习到能够从各种未知低光照条件下恢复正常图像的通用特征。本文提出将对比学习融入光照校正网络，在表示空间中学习抽象表示以区分不同的低光照条件，旨在增强网络的泛化能力。考虑到光照条件会改变图像的频率分量，我们在空间域和频率域中均进行表示的学习与比较，以充分利用对比学习的优势。所提方法在LOL和LOL-V2数据集上进行了评估，结果表明，与其他先进方法相比，该方法在定性和定量结果上均取得了更优表现。