🤖 AI Summary
To address the ill-posed inverse problem in single-pixel imaging, this paper proposes a Plug-and-Play Denoising Diffusion Implicit Model (PnP-DDIM) framework. Methodologically, we decouple the DDIM diffusion process into an interpretable three-stage paradigm—denoising, data-consistency correction, and sampling—and introduce a hybrid data-consistency module that linearly fuses multiple PnP fidelity terms to directly refine the denoiser output, thereby enhancing measurement consistency while preserving diffusion trajectory stability. Our key contribution is the first deep integration of the plug-and-play mechanism into the DDIM sampling procedure, enabling end-to-end co-optimization of learned priors and physical forward models. Experiments demonstrate significant improvements in reconstruction quality across multiple sampling rates (average PSNR gain of +1.8 dB), along with enhanced convergence robustness, outperforming state-of-the-art methods including PnP-ADMM and DiffPIR.
📝 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.