Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
arXiv:2602.23214v2 Announce Type: replace-cross 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 measureme
This research addresses fundamental limitations in current AI models for medical imaging, marking a critical advancement in robust and reliable reconstruction techniques.
Improved medical image reconstruction directly impacts diagnostic accuracy and patient outcomes, while also advancing the broader field of AI for inverse problems beyond healthcare.
The proposed dual-variable coupling method in diffusion models offers a more accurate and stable approach to PnP solvers, potentially leading to a new standard in medical imaging AI.
- · Medical AI researchers
- · Healthcare diagnostics industry
- · Patients receiving medical imaging
- · Generative AI model developers
- · Developers of less robust PnP solvers
More accurate and reliable medical diagnoses powered by AI.
Accelerated development and adoption of AI in various imaging modalities, including those with sparse or noisy data.
Potential for new medical imaging techniques that were previously limited by reconstruction challenges.
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Read at arXiv cs.LG