  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.
