利用用户个人照片库训练专属去噪模型，提升暗光图像质量。
Personalized Generative Low-light Image Denoising and Enhancement
- 基于用户相册构建个性化扩散模型，避免传统方法的幻觉问题。
- 在多种低光条件下，显著优于现有生成式去噪方法。
- 无需微调即可应用，适合手机相机、夜景拍摄等场景。
现代相机在低光条件下的表现受限于光子散粒噪声和传感器读出噪声的根本缺陷。生成式图像修复方法虽优于传统方法，但在信噪比（SNR）较低时易产生幻觉内容。借助用户个人相册的可用性，我们提出基于扩散的个性化生成去噪方法（DiffPGD），为每位用户构建定制化扩散模型。其核心创新在于设计了一种身份一致的物理属性缓冲区，从相册中提取人物的物理特征作为强先验，可无缝融入扩散模型，在无需微调的情况下恢复退化图像。在广泛的低光测试场景中，DiffPGD 的去噪与增强性能均显著优于现有扩散基方法。
原文摘要 · Abstract (English)
Modern cameras' performance in low-light conditions remains suboptimal due to fundamental limitations in photon shot noise and sensor read noise. Generative image restoration methods have shown promising results compared to traditional approaches, but they suffer from hallucinatory content generation when the signal-to-noise ratio (SNR) is low. Leveraging the availability of personalized photo galleries of the users, we introduce Diffusion-based Personalized Generative Denoising (DiffPGD), a new approach that builds a customized diffusion model for individual users. Our key innovation lies in the development of an identity-consistent physical buffer that extracts the physical attributes of the person from the gallery. This ID-consistent physical buffer serves as a robust prior that can be seamlessly integrated into the diffusion model to restore degraded images without the need for fine-tuning. Over a wide range of low-light testing scenarios, we show that DiffPGD achieves superior image denoising and enhancement performance compared to existing diffusion-based denoising approaches. Our project page can be found at \href{https://genai-restore.github.io/DiffPGD/}{\textcolor{purple}{\textbf{https://genai-restore.github.io/DiffPGD/}}}.
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