  Low-light image enhancement aims to improve an image's visibility while
keeping its visual naturalness. Different from existing methods tending to
accomplish the relighting task directly by ignoring the fidelity and
naturalness recovery, we investigate the intrinsic degradation and relight the
low-light image while refining the details and color in two steps. Inspired by
the color image formulation (diffuse illumination color plus environment
illumination color), we first estimate the degradation from low-light inputs to
simulate the distortion of environment illumination color, and then refine the
content to recover the loss of diffuse illumination color. To this end, we
propose a novel Degradation-to-Refinement Generation Network (DRGN). Its
distinctive features can be summarized as 1) A novel two-step generation
network for degradation learning and content refinement. It is not only
superior to one-step methods, but also capable of synthesizing sufficient
paired samples to benefit the model training; 2) A multi-resolution fusion
network to represent the target information (degradation or contents) in a
multi-scale cooperative manner, which is more effective to address the complex
unmixing problems. Extensive experiments on both the enhancement task and joint
detection task have verified the effectiveness and efficiency of our proposed
method, surpassing the SOTA by \textit{0.70dB on average and 3.18\% in mAP},
respectively. The code is available at
\url{https://github.com/kuijiang0802/DRGN}.
