• Corpus ID: 247839681

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging

@inproceedings{Kamilov2022PlugandPlayMF,
  title={Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging},
  author={Ulugbek S. Kamilov and Charles A. Bouman and Gregery T. Buzzard and Brendt Wohlberg},
  year={2022}
}
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned regularizer in the form of an image… 
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