  Recovering noise-covered details from low-light images is challenging, and
the results given by previous methods leave room for improvement. Recent
diffusion models show realistic and detailed image generation through a
sequence of denoising refinements and motivate us to introduce them to
low-light image enhancement for recovering realistic details. However, we found
two problems when doing this, i.e., 1) diffusion models keep constant
resolution in one reverse process, which limits the speed; 2) diffusion models
sometimes result in global degradation (e.g., RGB shift). To address the above
problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light
image enhancement. PyDiff uses a novel pyramid diffusion method to perform
sampling in a pyramid resolution style (i.e., progressively increasing
resolution in one reverse process). Pyramid diffusion makes PyDiff much faster
than vanilla diffusion models and introduces no performance degradation.
Furthermore, PyDiff uses a global corrector to alleviate the global degradation
that may occur in the reverse process, significantly improving the performance
and making the training of diffusion models easier with little additional
computational consumption. Extensive experiments on popular benchmarks show
that PyDiff achieves superior performance and efficiency. Moreover, PyDiff can
generalize well to unseen noise and illumination distributions.
