Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Plug-and-play (PnP) priors constitute 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 biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. 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 toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

Automated summary

Plug-and-play (PnP) priors are widely used frameworks for solving computational imaging problems by integrating physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data, resulting in state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term for data consistency and imposing a learned regularizer, such as an image denoiser.