This paper studies a diffusion-based framework to address the low-light image
enhancement problem. To harness the capabilities of diffusion models, we delve
into this intricate process and advocate for the regularization of its inherent
ODE-trajectory. To be specific, inspired by the recent research that low
curvature ODE-trajectory results in a stable and effective diffusion process,
we formulate a curvature regularization term anchored in the intrinsic
non-local structures of image data, i.e., global structure-aware
regularization, which gradually facilitates the preservation of complicated
details and the augmentation of contrast during the diffusion process. This
incorporation mitigates the adverse effects of noise and artifacts resulting
from the diffusion process, leading to a more precise and flexible enhancement.
To additionally promote learning in challenging regions, we introduce an
uncertainty-guided regularization technique, which wisely relaxes constraints
on the most extreme regions of the image. Experimental evaluations reveal that
the proposed diffusion-based framework, complemented by rank-informed
regularization, attains distinguished performance in low-light enhancement. The
outcomes indicate substantial advancements in image quality, noise suppression,
and contrast amplification in comparison with state-of-the-art methods. We
believe this innovative approach will stimulate further exploration and
advancement in low-light image processing, with potential implications for
other applications of diffusion models. The code is publicly available at
https://github.com/jinnh/GSAD.Comment: Accepted to NeurIPS 202