Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise. Existing supervised enlightening algorithms require a large set of pixel-aligned training image pairs, which are hard to prepare in practice. Though weakly-supervised or unsupervised methods can alleviate such challenges without using paired training images, some real-world artifacts inevitably get falsely amplified because of the lack of corresponded supervision. In this paper, instead of using perfectly aligned images for training, we creatively employ the misaligned real-world images as the guidance, which are considerably easier to collect. Specifically, we propose a Cross-Image Disentanglement Network (CIDN) to separately extract cross-image brightness and image-specific content features from low/normal-light images. Based on that, CIDN can simultaneously correct the brightness and suppress image artifacts in the feature domain, which largely increases the robustness to the pixel shifts. Furthermore, we collect a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions. Experimental results show that our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.

翻译：在低光状态下拍摄的图像受到低可见度和各种成像制品的影响,例如真实噪音。现有的受监督的启迪算法需要一套难以实际准备的大型像素结盟培训成像配对。虽然薄弱的受监督或不受监督的方法可以减轻这种挑战,但不使用配对培训成像,但一些现实世界的文物不可避免地会因为缺乏对应的监管而被错误地放大。在本文中,我们没有使用完全一致的图像进行培训,而是创造性地使用错误的真实世界图像作为指南,这很容易收集。具体地说,我们建议建立一个跨图像分离网络(CIDN),以便从低光/正常图像中单独提取交叉图像亮度和图像特定内容特征。在此基础上,CIDN可以同时纠正特征域的亮度并抑制图像制品,这在很大程度上增加了对像素转变的强度。此外,我们收集了一个新的低光图像增强图集,由与真实世界腐败的不匹配培训成像组成。实验结果显示我们拟议的低光数据,同时显示我们拟议的低光度模型的其他数据表现。