Oct
22
Date: 22-October-2026
Time: 10:00 AM ET (New York Time)
Presenter: Dr. Yu Sun
Based on the IEEE Xplore® article under the same title
Published IEEE Transactions on Computational Imaging, August 2024.
Download article: Original article will be made publicly available for download on the day of the webinar for 48 hours. ARTICLE LINK
About this topic:
Inverse problems are often ill-posed, meaning that multiple images may be consistent with the same set of measurements. As a result, solving an inverse problem requires not only recovering a high-quality image but also characterizing the ambiguity inherent in the reconstruction process. Diffusion models (DMs) have recently emerged as powerful image priors, delivering remarkable performance across a wide range of imaging applications. However, many existing DM-based approaches rely on approximations to the underlying generative process, which can lead to solutions that deviate from the true Bayesian posterior.
In this webinar, the presenter will present two diffusion-based sampling algorithms that leverage the expressive power of diffusion models without requiring such approximations. The proposed methods can be viewed as complementary sampling extensions of classical plug-and-play priors (PnP), with one based on gradient descent and the other on variable splitting. He will discuss the theoretical foundations of these algorithms, including distributional convergence guarantees, and demonstrate their performance on challenging inverse problems. In particular, results on black hole imaging highlight their ability to characterize multiple plausible solutions and perform uncertainty quantification in highly ill-posed settings.
About the presenter:
Dr. Yu Sun
Yu Sun (M’16) received the B.Eng in electronics and information from Sichuan University, Chengdu, China, and the Ph.D. degree in computer science from Washington University in St. Louis, St. Louis, MO, USA, in 2015 & 2022 respectively.
He is currently an Assistant Professor with the Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. Prior to joining Johns Hopkins, he was a Computing, Data \& Society Fellow with the Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA. His research focuses on explainable and reliable AI algorithms for scientific imaging and computer vision.
Dr. Sun’s doctoral dissertation received the Turner Dissertation Award in Computer Science at Washington University in St. Louis. He is a recipient of the NSF CAREER Award and a member of the IEEE Signal Processing Society's Computational Imaging Technical Committee.
