SCIEN and EE292E present “Computational Imaging: Reconciling Models and Learning”

Topic: 
Computational Imaging: Reconciling Models and Learning
Wednesday, February 3, 2021 - 4:30pm
Speaker: 
Dr. Ulugbek Kamilov (Washington Univ)
Abstract / Description: 

There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquisition. There are currently two distinct viewpoints on addressing such information loss: model-based and learning-based. Model-based methods leverage analytical signal properties (such as sparsity) and often come with theoretical guarantees and insights. Learning-based methods leverage flexible representations (such as convolutional neural nets) for best empirical performance through training on big datasets. The goal of this talk is to introduce a Regularization by Artifact Removal (RARE) framework that reconciles both viewpoints by providing the "deep learning prior" counterpart of the classical regularized inversion. This is achieved by specifying "artifact-removing deep neural nets" as a mechanism to infuse learned priors into recovery problems, while maintaining a clear separation between the prior and physics-based acquisition models. Our methodology can fully leverage the flexibility offered by deep learning by designing learned prior to be used within our new family of fast iterative algorithms. Yet, our results indicate that the such algorithms 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 application of the methodology to the problem to various biomedical imaging modalities, such as magnetic resonance imaging and intensity diffraction tomography.


Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

Bio: Ulugbek S. Kamilov is an Assistant Professor and Director of Computational Imaging Group (CIG) at Washington University in St. Louis. His research area is computational imaging with an emphasis on theory and algorithms for applications in biomedical imaging. His research interests include signal and image processing, machine learning, and optimization. He obtained the BSc and MSc degrees in Communication Systems, and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2008, 2011, and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA.

He is a recipient of the IEEE Signal Processing Society's 2017 Best Paper Award (with V. K. Goyal and S. Rangan). His Ph.D. thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He is serving as an Associate Editor for the IEEE Transactions on Computational Imaging (2019-present) and Biological Imaging (2020-present). He is also a member of IEEE Technical Committee on Computational Imaging (2016-2019, 2019-present).