Plug-and-play Diffusion Models for Image Compressive Sensing with Data Consistency Projection
This work addresses image reconstruction in compressive sensing, an incremental improvement for applications like single-pixel imaging.
The paper tackled the problem of image compressive sensing for single-pixel imaging by proposing a hybrid method that integrates Plug-and-Play techniques with diffusion models, achieving better reconstruction quality in experiments.
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.