  When capturing images in low-light conditions, the images often suffer from
low visibility, which not only degrades the visual aesthetics of images, but
also significantly degenerates the performance of many computer vision
algorithms. In this paper, we propose a self-supervised low-light image
enhancement framework (SID-NISM), which consists of two components, a
Self-supervised Image Decomposition Network (SID-Net) and a Nonlinear
Illumination Saturation Mapping function (NISM). As a self-supervised network,
SID-Net could decompose the given low-light image into its reflectance,
illumination and noise directly without any prior training or reference image,
which distinguishes it from existing supervised-learning methods greatly. Then,
the decomposed illumination map will be enhanced by NISM. Having the restored
illumination map, the enhancement can be achieved accordingly. Experiments on
several public challenging low-light image datasets reveal that the images
enhanced by SID-NISM are more natural and have less unexpected artifacts.
