We organize weekly AI meetings on zoom. We hold these events every Wednesday from 4 pm to 5 pm unless otherwise noted. We have guests from various universities and industries all over the world. Also, our graduate students present their progress some weeks. If you want to join us in these meetings, please email us at for the zoom info. 

May 31, 2022

Ulugbek Kamilov, Washington University in St. Louis

Computational Imaging: Integrating Physical and Learned Models using Plug-and-Play Methods

Computational imaging is a rapidly growing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as an inverse problem. Plug-and-Play Priors (PnP) is one of the most popular frameworks for solving computational imaging problems through the integration of physical and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods to provide state-of-the-art imaging algorithms. PnP algorithms alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned regularizer in the form of an “artifact-reducing” deep neural network. Recent highly successful applications of PnP algorithms include bio-microscopy, computerized tomography, magnetic resonance imaging, and joint ptycho-tomography. This talk 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.