Next Upcoming SPS Webinar Series: Signal Processing And Computational imagE formation (SPACE)

SPS Webinar Series: SPACE
(Signal Processing And Computational imagE formation)

SPS Webinar Series: SPACE | Confirmed Speakers (Series 1-15)

Upcoming Webinars @ SPACE
30 June 2020: Prof. Katie Bouman
14 July 2020: Prof. Jong Chul Ye

The IEEE Signal Processing Society would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic. We appreciate your continued patience and support as we work together to navigate these unforeseen and uncertain circumstances. We hope that you, your families, and your communities are safe!

Date: 30 June 2020

Speaker: Katherine L. (Katie) Bouman, Caltech
Title: Capturing the First Image of a Black Hole & Designing the Future of Black Hole Imaging

Abstract: This talk will present the methods and procedures used to produce the first image of a black hole from the Event Horizon Telescope, as well as discuss future developments. It had been theorized for decades that a black hole would leave a "shadow" on a background of hot gas. Taking a picture of this black hole shadow would help to address a number of important scientific questions, both on the nature of black holes and the validity of general relativity. Unfortunately, due to its small size, traditional imaging approaches require an Earth-sized radio telescope. In this talk, I discuss techniques the Event Horizon Telescope Collaboration has developed to photograph a black hole using the Event Horizon Telescope, a network of telescopes scattered across the globe. Imaging a black hole’s structure with this computational telescope required us to reconstruct images from sparse measurements, heavily corrupted by atmospheric error. This talk will summarize how the data from the 2017 observations were calibrated and imaged, and explain some of the challenges that arise with a heterogeneous telescope array like the EHT. The talk will also discuss future developments, including how we are developing machine learning methods to help design future telescope arrays.

About the presenter:

Katherine L. (Katie) Bouman is a Rosenberg Scholar and an assistant professor in the Computing and Mathematical Sciences and Electrical Engineering Department at the California Institute of Technology. Before joining Caltech, she was a postdoctoral fellow in the Harvard-Smithsonian Center for Astrophysics. She received her Ph.D. in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT in EECS. Before coming to MIT, she received her bachelor's degree in Electrical Engineering from the University of Michigan. The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging.

Date: 14 July 2020

Speaker: Jong Chul Ye, KAIST, Korea
Title: Optimal transport driven CycleGAN for unsupervised learning in inverse problems

Abstract: The penalized least squares (PLS) is a classic method to solve inverse problems, where a regularization term is added to stabilize the solution. Optimal transport (OT) is another mathematical framework that has recently received significant attention by computer vision community, for it provides means to transport one distribution to another in an unsupervised manner. The cycle-consistent generative adversarial network (cycleGAN) is a recent extension of GAN to learn target distributions with less mode collapsing behavior. Although similar in that no supervised training is required, the algorithms look different, so the mathematical relationship between these approaches is not clear.

In this talk, I explain an important advance to unveil the missing link. Specifically, we propose a novel PLS cost to measure the sum of distances in the measurement space and the latent space. When used as a transportation cost for optimal transport, we show that this new PLS cost leads to a novel cycleGAN architecture as a Kantorovich dual OT formulation. One of the most important advantages of this formulation is that depending on the knowledge of the forward problem, distinct variations of cycleGAN architecture can be derived. The new cycleGAN formulation have been applied for various imaging problems, such as accelerated magnetic resonance imaging (MRI), super-resolution/deconvolution microscopy, low-dose x-ray computed tomography (CT), satellite imagery, etc. Experimental results confirm the efficacy and flexibility of the theory.

About the presenter:

Jong Chul Ye is a Professor of the Dept. of Bio/Brain Engineering and Adjunct Professor at Dept. of Mathematical Sciences of Korea Advanced Institute of Science and Technology (KAIST), Korea. He received the B.Sc. and M.Sc. degrees from Seoul National University, Korea, and the Ph.D. from Purdue University, West Lafayette. Before joining KAIST, he was a Senior Research at Philips Research, GE Global Research in New York, and a postdoctoral fellow at University of Illinois at Urbana Champaign. He has served as an associate editor of IEEE Trans. on Image Processing, IEEE Trans. on Computational Imaging, and an editorial board member for Magnetic Resonance in Medicine.

He is currently an associate editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine. He is an IEEE Fellow, Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer. He was a General Co-chair for 2020 IEEE Symp. On Biomedical Imaging (ISBI) (with Mathews Jacob), and is a Program Co-Chair for 2014 ICASSP. His group was the first winner of the 2009 Recon Challenge at the ISMRM workshop with k-t FOCUSS algorithm, and the runner-up at 2016 Low Dose CT Grand Challenge organized by the American Association of Physicists in Medicine (AAPM) with the world’s first deep learning algorithm for low-dose CT reconstruction. His current research interests focus is deep learning theory and algorithms for various imaging reconstruction problems in x-ray CT, MRI, optics, ultrasound, remote sensing, etc.