  Previous raw image-based low-light image enhancement methods predominantly
relied on feed-forward neural networks to learn deterministic mappings from
low-light to normally-exposed images. However, they failed to capture critical
distribution information, leading to visually undesirable results. This work
addresses the issue by seamlessly integrating a diffusion model with a
physics-based exposure model. Different from a vanilla diffusion model that has
to perform Gaussian denoising, with the injected physics-based exposure model,
our restoration process can directly start from a noisy image instead of pure
noise. As such, our method obtains significantly improved performance and
reduced inference time compared with vanilla diffusion models. To make full use
of the advantages of different intermediate steps, we further propose an
adaptive residual layer that effectively screens out the side-effect in the
iterative refinement when the intermediate results have been already
well-exposed. The proposed framework can work with both real-paired datasets,
SOTA noise models, and different backbone networks. Note that, the proposed
framework is compatible with real-paired datasets, real/synthetic noise models,
and different backbone networks. We evaluate the proposed method on various
public benchmarks, achieving promising results with consistent improvements
using different exposure models and backbones. Besides, the proposed method
achieves better generalization capacity for unseen amplifying ratios and better
performance than a larger feedforward neural model when few parameters are
adopted.
