mlsanityData to 7 Oct 2026

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

arXiv 2509.09365 ↗First posted 11 Sep 2025cs.CVcited by 0

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 14 of its 19 references that are in the map. First posted on arXiv 11 Sep 2025.

Builds on

14 of 19 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences14 Mar
  2. 2024A Survey on Diffusion Models for Inverse Problems30 Sep
  3. 2023Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance27 Dec
  4. 2023Denoising Diffusion Models for Plug-and-Play Image Restoration15 May
  5. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model1 Dec
  6. 2022Diffusion Posterior Sampling for General Noisy Inverse Problems29 Sep
  7. 2022Denoising Diffusion Restoration Models27 Jan

Other references · 7

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  6. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  7. 2020Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imagingcited by 78

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Close in wording, no citation either way

  1. 2025Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models
  2. 2025Plug-and-Play Posterior Sampling for Blind Inverse Problems
  3. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
  4. 2026Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
  5. 2024Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method
  6. 2025MACS: Measurement-Aware Consistency Sampling for Inverse Problems
  7. 2024Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
  8. 2026Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems