  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 denoiser. Recent highly-successful
applications of PnP algorithms include bio-microscopy, computerized tomography,
magnetic resonance imaging, and joint ptycho-tomography. This article presents
a unified and principled review of PnP by tracing its roots, describing its
major variations, summarizing main results, and discussing applications in
computational imaging. We also point the way towards further developments by
discussing recent results on equilibrium equations that formulate the problem
associated with PnP algorithms.
