Real-Time Unknown-View Tomography Using Recurrent Neural Networks with Applications to Keyhole Imaging

Christopher A. Metzler; Gordon Wetzstein

doi:10.1364/COSI.2020.CTh5C.1

Imaging and Applied Optics Congress
OSA Technical Digest (Optica Publishing Group, 2020),
paper CTh5C.1
•https://doi.org/10.1364/COSI.2020.CTh5C.1

Real-Time Unknown-View Tomography Using Recurrent Neural Networks with Applications to Keyhole Imaging

Christopher A. Metzler and Gordon Wetzstein

Computational Optical Sensing and Imaging 2020

Washington, DC United States
22–26 June 2020
ISBN: 978-1-943580-77-4

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Non-Line-of-Sight Imaging (CTh5C)

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C. A. Metzler and G. Wetzstein, "Real-Time Unknown-View Tomography Using Recurrent Neural Networks with Applications to Keyhole Imaging," in Imaging and Applied Optics Congress, OSA Technical Digest (Optica Publishing Group, 2020), paper CTh5C.1.

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Abstract

Unknown-view tomography is an important but computationally intensive reconstruction problem. We demonstrate that recurrent neural networks (RNNs) can perform unknown-view tomography in real time and validate our solution on simulated non-line-of- sight imaging-through-a-keyhole data.

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