Abstract
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.
Abstract (translated)
在低光环境下拍摄的图像往往会出现视野不佳的情况，这可能会降低图像质量，甚至影响后续任务的表现。基于卷积神经网络的方法很难学习通用的特征，以便从各种未知低光环境下恢复正常图像。在本文中，我们提出将对比学习融入照明纠正网络中，学习抽象表示来在表示空间中区分各种低光条件，以增强网络的泛化能力。考虑到光照条件可以改变图像的频谱成分，我们将在空间域和频域中学习表示并进行比较，以充分利用对比学习的优势。我们使用LOL和LOL-V2数据集评估了所选方法，结果表明，与其他先进技术相比，该方法取得了更好的质量和定量结果。
URL
https://arxiv.org/abs/2303.13412
