Hashemi, Connor2024-02-092024-02-092023-12https://hdl.handle.net/11299/260635University of Minnesota Ph.D. dissertation. December 2023. Major: Electrical Engineering. Advisor: James Leger. 1 computer file (PDF); x, 166 pages.In recent years, significant progress has been made in passive non-line-of-sight imaging, which looks around corners at hidden objects using just scattered light off a rough surface. Since passive non-line-of-sight imaging can obtain information about the surrounding environment that was previously deemed irrecoverable, it has many far-reaching applications, such as improving autonomous vehicles, aiding search-and-rescue operations, and performing military surveillance. While the bulk of progress has focused on improving the resolution and capabilities of existing non-line-of-sight imaging algorithms, most methods assume a high signal-to-noise ratio and low amounts of background signals. These conditions are impossible to find outside of highly-controlled laboratories and do not correspond to real-world applications. This thesis considers "low-signal'' passive non-line-of-sight imaging, where the desired imaging signal is either swamped by stochastic noise or structured background signals that interfere with conventional non-line-of-sight reconstructions. These scenarios mimic what is found in real-world environments. To image in these difficult low-signal scenarios, this thesis delves into two main works: unmixing spectral content of the scattered light and denoising thermal scattered imagery. As a result of these methods, non-line-of-sight imaging can be performed in many scenarios previously deemed too difficult, and future low-signal imaging can build off the principles laid in this thesis.encomputational imagingcomputer visioninverse problemslow-signal imagingnon-line-of-sight imagingopticsThesis or Dissertation