ISCS23: Plug-and-Play Models for Large-Scale Computational Imaging

"Plug-and-Play Models for Large-Scale Computational Imaging " by Prof. Ulugbek Kamilov (Washington University in St. Louis, USA)

Abstract: 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 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 models alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned image prior in the form of an “image denoising” deep neural network. This talk presents a principled discussion of PnP, its theoretical foundations, its implementations for large-scale imaging problems, and recent results on PnP for the recovery of continuously represented images. We present several applications of our theoretical and algorithmic insights in bio-microscopy, computerized tomography, and magnetic resonance imaging.