![[2602.23214] Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.23214] Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
Summary
This paper presents a novel approach to medical image reconstruction using Dual-Coupled Plug-and-Play Diffusion, addressing limitations in existing methods and achieving state-of-the-art results.
Why It Matters
The research addresses critical challenges in medical imaging, particularly in enhancing image quality under heavy corruption. By improving reconstruction fidelity and convergence speed, this work has significant implications for clinical practices and diagnostic accuracy.
Key Takeaways
- Introduces Dual-Coupled PnP Diffusion to improve image reconstruction.
- Addresses the limitations of existing PnP solvers by incorporating historical data.
- Presents Spectral Homogenization to mitigate artifacts in reconstructed images.
- Demonstrates state-of-the-art fidelity in CT and MRI reconstruction.
- Achieves faster convergence rates compared to traditional methods.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23214 (cs) [Submitted on 26 Feb 2026] Title:Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction Authors:Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu, Jingyi Yu, Hongjiang Wei, Yuyao Zhang View a PDF of the paper titled Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction, by Chenhe Du and 6 other authors View PDF HTML (experimental) Abstract:Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors. However, we identify a critical flaw in prevailing PnP solvers (e.g., based on HQS or Proximal Gradient): they function as memoryless operators, updating estimates solely based on instantaneous gradients. This lack of historical tracking inevitably leads to non-vanishing steady-state bias, where the reconstruction fails to strictly satisfy physical measurements under heavy corruption. To resolve this, we propose Dual-Coupled PnP Diffusion, which restores the classical dual variable to provide integral feedback, theoretically guaranteeing asymptotic convergence to the exact data manifold. However, this rigorous geometric coupling introduces a secondary challenge: the accumulated dual residuals exhibit spectrally colored, structured artifacts that violate the Additive White Gaussian Noise (AWGN) assump...
