Ulugbek Kamilov: Computational Imaging: Integrating Physical and Learned Models скачать в хорошем качестве

Ulugbek Kamilov: Computational Imaging: Integrating Physical and Learned Models

Recording of Ulugbek Kamilov (Washington University in St. Louis) talk on December 19, 2022, at the EPFL Seminar Series in Imaging. 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 and recent results on PnP under inexact physical and learned models. Inexact models arise naturally in computational imaging when using approximate physical models for efficiency or when test images are from a different distribution than images used for training. We present several successful applications of our theoretical and algorithmic insights in bio-microscopy, computerized tomography, and magnetic resonance imaging.