用物理先验指导扩散模型，提升暗光图像增强效果。
Diffusion-Based Low-Light Image Enhancement with Color and Luminance Priors
- 分解暗光图四成分，作为扩散模型的控制信号。
- 在多个数据集上达到领先性能，无需微调。
- 适合需要高质量图像增强的视觉任务使用。
暗光图像常因对比度低、噪声大和色彩失真而影响视觉质量与下游任务表现。本文提出一种基于条件扩散的增强框架，引入结构化控制嵌入模块（SCEM），将输入图像分解为光照、光照不变特征、阴影先验和色彩不变线索四个组件。这些成分作为控制信号，引导基于U-Net的扩散模型进行增强。模型仅在LOLv1上训练，未做微调即在LOLv2-real、LSRW、DICM、MEF和LIME上评估，定量与感知指标均达当前最优，展现出强大泛化能力。
原文摘要 · Abstract (English)
Low-light images often suffer from low contrast, noise, and color distortion, degrading visual quality and impairing downstream vision tasks. We propose a novel conditional diffusion framework for low-light image enhancement that incorporates a Structured Control Embedding Module (SCEM). SCEM decomposes a low-light image into four informative components including illumination, illumination-invariant features, shadow priors, and color-invariant cues. These components serve as control signals that condition a U-Net-based diffusion model trained with a simplified noise-prediction loss. Thus, the proposed SCEM equipped Diffusion method enforces structured enhancement guided by physical priors. In experiments, our model is trained only on the LOLv1 dataset and evaluated without fine-tuning on LOLv2-real, LSRW, DICM, MEF, and LIME. The method achieves state-of-the-art performance in quantitative and perceptual metrics, demonstrating strong generalization across benchmarks. https://casted.github.io/scem/.
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