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arXiv · 2509.09365

Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection

Xiaodong Wang·Ping Wang·Zhangyuan Li·Xin Yuan
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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.

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Xiaodong Wang, Ping Wang, Zhangyuan Li, Xin Yuan. 2025-09-11. Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection. https://arxiv.org/abs/2509.09365

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