THESIS DEFENSE: Extreme Imaging via Physical Model Inversion: Seeing Around Corners and Imaging Black Holes
Speaker
Katie Bouman
Thesis supervisor: Prof. William T. Freeman
Committee: Prof. Polina Golland, Dr. Sheperd Doeleman
Abstract:
Imaging often plays a critical role in advancing fundamental science. However, as science continues to push the boundaries of knowledge, imaging systems are reaching the limits of what can be measured using traditional-direct approaches. By designing systems that tightly integrate novel sensor and algorithm design, it may be possible to develop imaging systems that exceed fundamental theoretical limitations to observe things previously impossible to see.
In this talk, we focus on two imaging problems that explicitly leverage structure in our universe: reconstructing images and video from a computational telescope the size of the Earth, and seeing around corners. For the first imaging problem, we investigate ways to reconstruct images and video from a sparse telescope array distributed around the globe. Additionally, we present an evaluation process developed to rigorously evaluate imaging methods in order to establish confidence in reconstructions done with real scientific data. The methods and evaluation techniques developed in this work will hopefully aid in ongoing work to take the first picture of a black hole. Next, we briefly discuss methods developed for using the subtle spatio-temporal radiance variations that arise on the ground at the base of an edge to construct a one-dimensional video of a hidden scene. These methods may be especially valuable in remotely sensing occupants in a room during search and rescue operations, or in detecting hidden, oncoming vehicles and/or pedestrians for collision avoidance systems.
Committee: Prof. Polina Golland, Dr. Sheperd Doeleman
Abstract:
Imaging often plays a critical role in advancing fundamental science. However, as science continues to push the boundaries of knowledge, imaging systems are reaching the limits of what can be measured using traditional-direct approaches. By designing systems that tightly integrate novel sensor and algorithm design, it may be possible to develop imaging systems that exceed fundamental theoretical limitations to observe things previously impossible to see.
In this talk, we focus on two imaging problems that explicitly leverage structure in our universe: reconstructing images and video from a computational telescope the size of the Earth, and seeing around corners. For the first imaging problem, we investigate ways to reconstruct images and video from a sparse telescope array distributed around the globe. Additionally, we present an evaluation process developed to rigorously evaluate imaging methods in order to establish confidence in reconstructions done with real scientific data. The methods and evaluation techniques developed in this work will hopefully aid in ongoing work to take the first picture of a black hole. Next, we briefly discuss methods developed for using the subtle spatio-temporal radiance variations that arise on the ground at the base of an edge to construct a one-dimensional video of a hidden scene. These methods may be especially valuable in remotely sensing occupants in a room during search and rescue operations, or in detecting hidden, oncoming vehicles and/or pedestrians for collision avoidance systems.