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.

翻译：本文研究了一种基于扩散模型的框架，以解决低光图像增强问题。为发挥扩散模型的能力，我们深入探究其复杂过程，并提出对其固有常微分方程（ODE）轨迹进行正则化。具体而言，受近期关于低曲率ODE轨迹能产生稳定有效扩散过程的研究启发，我们构建了一个基于图像数据内在非局部结构的曲率正则化项，即全局结构感知正则化，该正则化在扩散过程中逐步促进复杂细节的保留和对比度的增强。这种整合减轻了扩散过程中噪声和伪影的不利影响，从而实现更精确、更灵活的增强效果。为进一步促进困难区域的学习，我们引入了一种不确定性引导的正则化技术，该技术明智地放松了对图像中最极端区域的约束。实验评估表明，所提出的基于扩散模型的框架，辅以秩感知正则化，在低光增强中取得了显著性能。与最先进方法相比，研究结果在图像质量、噪声抑制和对比度放大方面均展现出实质性进展。我们相信，这种创新方法将激发低光图像处理领域的进一步探索和发展，并对扩散模型的其他应用具有潜在影响。代码公开于 https://github.com/jinnh/GSAD。