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 two traditionally distinct paradigms for designing computational imaging methods: model-based and learning-based. Model-based methods rely on analytical signal properties and often come with theoretical guarantees and insights. Learning-based methods use 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 the imaging problem, while maintaining a clear separation between the image prior and physics-based sensor 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, CT, and optical microscopy.
Biography
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 PhD 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. 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 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 and is a program co-chair for BASP 2023. He has co-organized several large computational-imaging workshops, including IMA Special Workshop on Computational Imaging in 2019, Learning for Computational Imaging (LCI) Workshop at ICCV 2021, IEEE International Workshop on Computational Cameras and Displays (CCD) at CVPR 2022.