用扩散模型解决未知退化图像复原，无需事先知道退化方式。
Plug-and-Play Posterior Sampling for Blind Inverse Problems
- 将盲逆问题转为交替去噪过程，用两个扩散模型分别建模图像和退化参数。
- 在盲去模糊任务中，定量指标和视觉效果均优于现有方法。
- 适合需要灵活适应未知退化场景的研究者或工程应用。
我们提出盲插件式扩散模型（Blind-PnPDM），用于解决目标图像和测量算子均未知的盲逆问题。与依赖显式先验或单独参数估计的传统方法不同，本方法通过将问题重构为交替高斯去噪过程，实现后验采样。利用两个扩散模型作为学习先验：一个捕捉目标图像分布，另一个表征测量算子参数。这种扩散模型的插件式集成确保了灵活性和易适配性。在盲图像去模糊任务上的实验表明，Blind-PnPDM 在定量指标和视觉保真度方面均优于现有最先进方法。结果证明，将盲逆问题视为一系列去噪子问题，并结合扩散模型的强大表达能力，具有显著有效性。
原文摘要 · Abstract (English)
We introduce Blind Plug-and-Play Diffusion Models (Blind-PnPDM) as a novel framework for solving blind inverse problems where both the target image and the measurement operator are unknown. Unlike conventional methods that rely on explicit priors or separate parameter estimation, our approach performs posterior sampling by recasting the problem into an alternating Gaussian denoising scheme. We leverage two diffusion models as learned priors: one to capture the distribution of the target image and another to characterize the parameters of the measurement operator. This PnP integration of diffusion models ensures flexibility and ease of adaptation. Our experiments on blind image deblurring show that Blind-PnPDM outperforms state-of-the-art methods in terms of both quantitative metrics and visual fidelity. Our results highlight the effectiveness of treating blind inverse problems as a sequence of denoising subproblems while harnessing the expressive power of diffusion-based priors.
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