Computational Imaging: Integrating Physical and Learned Models
Event details
| Date | 19.12.2022 |
| Hour | 17:30 › 18:30 |
| Speaker | Prof. Ulugbek Kamilov, Washington University in St. Louis |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
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.
Biography
Ulugbek S. Kamilov is the Director of Computational Imaging Group and an Assistant Professor of Electrical & Systems Engineering and Computer Science & Engineering at Washington University in St. Louis. He obtained the BSc/MSc degree in Communication Systems and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2011 and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. He is a recipient of the NSF CAREER Award and the IEEE Signal Processing Society’s 2017 Best Paper Award. He was among 55 early-career researchers in the USA selected as a Fellow for the Scialog initiative on “Advancing Bioimaging” in 2021. His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He has served as a Senior Member of the Editorial Board of IEEE Signal Processing Magazine and as an Associate Editor of IEEE Transactions on Computational Imaging. He has served on IEEE Signal Processing Society’s Computational Imaging Technical Committee and Bioimaging and Signal Processing Technical Committee. He was a plenary speaker at iTWIST 2018 and is a program co-chair for the International Biomedical and Astronomical Signal Processing Frontiers conference for 2023.
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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.
Biography
Ulugbek S. Kamilov is the Director of Computational Imaging Group and an Assistant Professor of Electrical & Systems Engineering and Computer Science & Engineering at Washington University in St. Louis. He obtained the BSc/MSc degree in Communication Systems and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2011 and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. He is a recipient of the NSF CAREER Award and the IEEE Signal Processing Society’s 2017 Best Paper Award. He was among 55 early-career researchers in the USA selected as a Fellow for the Scialog initiative on “Advancing Bioimaging” in 2021. His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He has served as a Senior Member of the Editorial Board of IEEE Signal Processing Magazine and as an Associate Editor of IEEE Transactions on Computational Imaging. He has served on IEEE Signal Processing Society’s Computational Imaging Technical Committee and Bioimaging and Signal Processing Technical Committee. He was a plenary speaker at iTWIST 2018 and is a program co-chair for the International Biomedical and Astronomical Signal Processing Frontiers conference for 2023.
Register here!
Practical information
- General public
- Free
Organizer
- EPFL Center for Imaging