将插件式先验方法重新理解为基于得分的模型，实现无需重训练的高效图像重建。
Plug-and-Play Priors as a Score-Based Method
- 把经典插件式先验看作得分模型，打通物理测量与深度去噪器的桥梁。
- 首次在相同网络先验下实现PnP与得分扩散模型的直接对比实验。
- 可直接复用现成得分扩散模型，提升图像逆问题求解效率与灵活性。
插件式（PnP）方法通过融合物理测量模型与预训练深度去噪器作为先验，广泛应用于图像逆问题求解。最近，基于得分的扩散模型（SBMs）通过训练深度去噪器以表示图像先验的得分，成为强大的图像生成框架。尽管PnP与SBMs均使用深度去噪器，但其得分性质在文献中未被充分探索，因二者起源分别基于近端优化与得分匹配。本文提出一种新视角：将PnP视为得分模型，使现有强大SBMs可直接用于经典PnP算法，无需重新训练。我们建立了适配主流SBMs作为PnP先验的一系列数学关系，并验证该方法可在相同神经网络先验下，实现PnP与基于SBM的重建方法的直接比较。代码已公开于 https://github.com/wustl-cig/score_pnp。
原文摘要 · Abstract (English)
Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-based diffusion models (SBMs) have recently emerged as a powerful framework for image generation by training deep denoisers to represent the score of the image prior. While both PnP and SBMs use deep denoisers, the score-based nature of PnP is unexplored in the literature due to its distinct origins rooted in proximal optimization. This letter introduces a novel view of PnP as a score-based method, a perspective that enables the re-use of powerful SBMs within classical PnP algorithms without retraining. We present a set of mathematical relationships for adapting popular SBMs as priors within PnP. We show that this approach enables a direct comparison between PnP and SBM-based reconstruction methods using the same neural network as the prior. Code is available at https://github.com/wustl-cig/score_pnp.
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