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.
@article{arxiv.2412.11108,
title = {Plug-and-Play Priors as a Score-Based Method},
author = {Chicago Y. Park and Yuyang Hu and Michael T. McCann and Cristina Garcia-Cardona and Brendt Wohlberg and Ulugbek S. Kamilov},
journal= {arXiv preprint arXiv:2412.11108},
year = {2025}
}