Abstract
We explore the connection between Plug-and-Play (PnP) methods and Denoising Diffusion Implicit Models (DDIM) for solving ill-posed inverse problems, with a focus on single-pixel imaging. We begin by identifying key distinctions between PnP and diffusion models-particularly in their denoising mechanisms and sampling procedures. By decoupling the diffusion process into three interpretable stages: denoising, data consistency enforcement, and sampling, we provide a unified framework that integrates learned priors with physical forward models in a principled manner. Building upon this insight, we propose a hybrid data-consistency module that linearly combines multiple PnP-style fidelity terms. This hybrid correction is applied directly to the denoised estimate, improving measurement consistency without disrupting the diffusion sampling trajectory. Experimental results on single-pixel imaging tasks demonstrate that our method achieves better reconstruction quality.
Abstract (translated)
我们探讨了插件即玩（Plug-and-Play，PnP）方法与去噪扩散隐式模型（Denoising Diffusion Implicit Models，DDIM）在解决病态逆问题中的联系，特别是单像素成像。首先，我们识别出PnP和扩散模型之间的一些关键区别，特别是在它们的去噪机制和采样程序方面。通过将扩散过程分解为三个可解释的阶段：去噪、数据一致性执行和采样，我们提供了一个统一的框架，在这个框架中以一种原则性的方式整合了学习到的先验与物理前向模型。在此洞察的基础上，我们提出了一种混合的数据一致性模块，该模块通过线性组合多个PnP风格的一致性项来直接应用于去噪估计上。这种混合校正是在不干扰扩散采样轨迹的情况下提高了测量一致性。实验结果表明，在单像素成像任务中，我们的方法实现了更好的重建质量。
URL
https://arxiv.org/abs/2509.09365
