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Abstract

Computational imaging is a rapidly growing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as an inverse problem. There are currently two distinct approaches for designing computational imaging methods: model-based and learning-based. Model-based methods leverage analytical signal properties and often come with theoretical guarantees and insights. Learning-based methods leverage data-driven representations for best empirical performance through training on large datasets. This talk presents Regularization by Artifact Removal (RARE), as a framework for reconciling both viewpoints by providing a “deep learning” extension to the classical theory. RARE uses pre-trained “artifact-removing deep neural nets” for infusing a learned prior knowledge into an inverse problem while maintaining a clear separation between the prior and physics-based acquisition model. Our results indicate that RARE can achieve state-of-the-art performance in different computational imaging tasks, while also being amenable to rigorous theoretical analysis. We will focus on the latest theoretical insights and applications of RARE in biomedical imaging, including MRI and CT.

Bio

Ulugbek S. Kamilov is Assistant Professor and Director of Computational Imaging Group (CIG) at Washington University in St. Louis. He obtained the BSc/MSc degree in Communication Systems and the Ph.D. degree in Electrical Engineering from EPFL, Switzerland, in 2011 and 2015, respectively. From 2015 to 2017, he was a Research Scientist at MERL, Cambridge, MA, USA. He is a recipient of the NSF CAREER Award in 2021 and the IEEE Signal Processing Society’s 2017 Best Paper Award. His Ph.D. thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He has served as an Associate Editor of IEEE Transactions on Computational Imaging (2019-present), Biological Imaging (2020-present), and on IEEE Signal Processing Society’s Computational Imaging Technical Committee (2016-2021). He was a plenary speaker at iTWIST 2018, has co-organized IMA Special Workshop on Computational Imaging in 2019, and is a program co-chair for SampTA 2021 and BASP 2022.

Hosts

Professor Charles Bouman and Professor Stanley Chan