Plug-and-Play Priors as a Score-Based Method
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909821851140096 |
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| author | Park, Chicago Y. Hu, Yuyang McCann, Michael T. Garcia-Cardona, Cristina Wohlberg, Brendt Kamilov, Ulugbek S. |
| author_facet | Park, Chicago Y. Hu, Yuyang McCann, Michael T. Garcia-Cardona, Cristina Wohlberg, Brendt Kamilov, Ulugbek S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Plug-and-Play Priors as a Score-Based Method Park, Chicago Y. Hu, Yuyang McCann, Michael T. Garcia-Cardona, Cristina Wohlberg, Brendt Kamilov, Ulugbek S. Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Plug-and-Play Priors as a Score-Based Method |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.11108 |