Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED. It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains and successfully alleviates the dependence on pairwise training data via zero-reference learning. Specifically, we first design the initial optimization network to preprocess the input image and implement bidirectional constraints between the diffusion model and the initial optimization network through multiple objective functions. Subsequently, the degradation factors of the real-world scene are optimized iteratively to achieve effective light enhancement. In addition, we explore a frequency-domain based and semantically guided appearance reconstruction module that encourages feature alignment of the recovered image at a fine-grained level and satisfies subjective expectations. Finally, extensive experiments demonstrate the superiority of our approach to other state-of-the-art methods and more significant generalization capabilities. We will open the source code upon acceptance of the paper.

翻译：基于扩散模型的低光照图像增强方法严重依赖成对训练数据，导致其广泛应用受限。同时，现有的无监督方法缺乏对未知退化的有效桥接能力。为应对这些局限性，我们提出了一种新颖的用于低光照图像增强的零参考光照估计扩散模型，称为Zero-LED。它利用扩散模型的稳定收敛能力来弥合低光照域与真实正常光照域之间的差距，并通过零参考学习成功减轻了对成对训练数据的依赖。具体而言，我们首先设计了初始优化网络对输入图像进行预处理，并通过多个目标函数实现扩散模型与初始优化网络之间的双向约束。随后，对真实场景的退化因子进行迭代优化，以实现有效的光照增强。此外，我们探索了一种基于频域和语义引导的外观重建模块，该模块在细粒度层面鼓励恢复图像的特征对齐，并满足主观期望。最后，大量实验证明了我们的方法相较于其他最先进方法的优越性以及更显著的泛化能力。论文录用后我们将公开源代码。