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arXiv · 2412.11108

Plug-and-Play Priors as a Score-Based Method

Chicago Y. Park·Yuyang Hu·Michael T. McCann·Cristina Garcia-Cardona·Brendt Wohlberg·Ulugbek S. Kamilov
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Abstract

Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-based diffusion models (SBMs) have recently emerged as a powerful framework for image generation by training deep denoisers to represent the score of the image prior. While both PnP and SBMs use deep denoisers, the score-based nature of PnP is unexplored in the literature due to its distinct origins rooted in proximal optimization. This letter introduces a novel view of PnP as a score-based method, a perspective that enables the re-use of powerful SBMs within classical PnP algorithms without retraining. We present a set of mathematical relationships for adapting popular SBMs as priors within PnP. We show that this approach enables a direct comparison between PnP and SBM-based reconstruction methods using the same neural network as the prior. Code is available at https://github.com/wustl-cig/score_pnp.

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Chicago Y. Park, Yuyang Hu, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ulugbek S. Kamilov. 2024-12-15. Plug-and-Play Priors as a Score-Based Method. https://doi.org/10.1109/icip55913.2025.11084503

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