LLDiffusion: Learning Degradation Representations in Diffusion Models
  for Low-Light Image Enhancement
Tao Wang Kaihao Zhang Ziqian Shao Wenhan Luo Bjorn Stenger Tae-Kyun Kim Wei Liu Hongdong Li
Current deep learning methods for low-light image enhancement (LLIE)
typically rely on pixel-wise mapping learned from paired data. However, these
methods often overlook the importance of considering degradation
representations, which can lead to sub-optimal outcomes. In this paper, we
address this limitation by proposing a degradation-aware learning scheme for
LLIE using diffusion models, which effectively integrates degradation and image
priors into the diffusion process, resulting in improved image enhancement. Our
proposed degradation-aware learning scheme is based on the understanding that
degradation representations play a crucial role in accurately modeling and
capturing the specific degradation patterns present in low-light images. To
this end, First, a joint learning framework for both image generation and image
enhancement is presented to learn the degradation representations. Second, to
leverage the learned degradation representations, we develop a Low-Light
Diffusion model (LLDiffusion) with a well-designed dynamic diffusion module.
This module takes into account both the color map and the latent degradation
representations to guide the diffusion process. By incorporating these
conditioning factors, the proposed LLDiffusion can effectively enhance
low-light images, considering both the inherent degradation patterns and the
desired color fidelity. Finally, we evaluate our proposed method on several
well-known benchmark datasets, including synthetic and real-world unpaired
datasets. Extensive experiments on public benchmarks demonstrate that our
LLDiffusion outperforms state-of-the-art LLIE methods both quantitatively and
qualitatively. The source code and pre-trained models are available at
https://github.com/TaoWangzj/LLDiffusion.