  Low-light image enhancement (LLIE) techniques attempt to increase the
visibility of images captured in low-light scenarios. However, as a result of
enhancement, a variety of image degradations such as noise and color bias are
revealed. Furthermore, each particular LLIE approach may introduce a different
form of flaw within its enhanced results. To combat these image degradations,
post-processing denoisers have widely been used, which often yield oversmoothed
results lacking detail. We propose using a diffusion model as a post-processing
approach, and we introduce Low-light Post-processing Diffusion Model (LPDM) in
order to model the conditional distribution between under-exposed and
normally-exposed images. We apply LPDM in a manner which avoids the
computationally expensive generative reverse process of typical diffusion
models, and post-process images in one pass through LPDM. Extensive experiments
demonstrate that our approach outperforms competing post-processing denoisers
by increasing the perceptual quality of enhanced low-light images on a variety
of challenging low-light datasets. Source code is available at
https://github.com/savvaki/LPDM.
