Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …

Retinex model-based methods have shown to be effective in layer-wise manipulation with
well-designed priors for low-light image enhancement. However, the commonly used hand …

Existing low-light image enhancement techniques are mostly not only difficult to deal with
both visual quality and computational efficiency but also commonly invalid in unknown …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …

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 …