Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative
  Diffusion Model
Xunpeng Yi Han Xu Hao Zhang Linfeng Tang Jiayi Ma
In this paper, we rethink the low-light image enhancement task and propose a
physically explainable and generative diffusion model for low-light image
enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the
physical model and the generative network. Furthermore, we hope to supplement
and even deduce the information missing in the low-light image through the
generative network. Therefore, Diff-Retinex formulates the low-light image
enhancement problem into Retinex decomposition and conditional image
generation. In the Retinex decomposition, we integrate the superiority of
attention in Transformer and meticulously design a Retinex Transformer
decomposition network (TDN) to decompose the image into illumination and
reflectance maps. Then, we design multi-path generative diffusion networks to
reconstruct the normal-light Retinex probability distribution and solve the
various degradations in these components respectively, including dark
illumination, noise, color deviation, loss of scene contents, etc. Owing to
generative diffusion model, Diff-Retinex puts the restoration of low-light
subtle detail into practice. Extensive experiments conducted on real-world
low-light datasets qualitatively and quantitatively demonstrate the
effectiveness, superiority, and generalization of the proposed method.