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

• URL: http://arxiv.org/abs/2509.09365v1
• Date: Thu, 11 Sep 2025 11:30:31 GMT
• Title: Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
• Authors: Xiaodong Wang, Ping Wang, Zhangyuan Li, Xin Yuan,
• Abstract summary: We explore the connection between Plug-and-Play (Play) methods and Denoising Implicit Implicit Models (DDIM)<n>We provide a unified framework that integrates learned priors with physical forward models in principled manner.
• Score: 11.296566218142521
• License: http://creativecommons.org/licenses/by/4.0/
• 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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