Machine learning (ML) and statistical signal processing provide a powerful lens through which to develop and understand new imaging techniques. Together they allow one to abstract complex physical systems into manageable representations that can leverage new kinds of models and algorithms, such as deep learning. When used appropriately, ML-based imaging systems enable a host of new capabilities, from non-line-of-sight imaging to imaging in scattering media. These advancements have wide-sweeping implications in scientific imaging, medical imaging, consumer photography, navigation, security, and more. In this talk I will present new ML-based systems for (1) high-resolution, photon-efficient, non-line-of-sight imaging with a continuous wave laser and (2) imaging and tracking non-line-of-sight objects from time-of-flight measurements captured through a door's keyhole.
Chris Metzler is an Intelligence Community Postdoctoral Research Fellow in the Stanford Computational Imaging Lab. Prior to this, he was an NSF Graduate Research Fellow, a DoD NDSEG Fellow, and a NASA Texas Space Grant Consortium Fellow in the Digital Signal Processing and Computational Imaging Labs at Rice University. His research develops data-driven solutions to challenging imaging problems.
Host: Elfar Adalsteinsson