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.

通过提出一种物理可解释的、生成扩散模型 (Diff-Retinex) 来重新思考低光图像增强任务，将低光图像增强问题形式化为 Retinex 分解和条件图像生成，并通过生成扩散模型实现对低光细节的修复。在真实世界低光数据集上进行的广泛实验定性和定量地证明了所提方法的有效性、优越性和泛化性。