Solving Inverse Problems using Self-Supervised Deep Neural Nets

Jiapeng Liu; Muralidhar M. Balaji; Christopher A. Metzler; M. Salman Asif; Prasanna Rangarajan

doi:10.1364/COSI.2021.CTh5A.2

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper CTh5A.2
•https://doi.org/10.1364/COSI.2021.CTh5A.2

Solving Inverse Problems using Self-Supervised Deep Neural Nets

Jiapeng Liu, Muralidhar M. Balaji, Christopher A. Metzler, M. Salman Asif, and Prasanna Rangarajan

Computational Optical Sensing and Imaging 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Inversion Methods for Computational Imaging (CTh5A)

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J. Liu, M. M. Balaji, C. A. Metzler, M. S. Asif, and P. Rangarajan, "Solving Inverse Problems using Self-Supervised Deep Neural Nets," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper CTh5A.2.

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Abstract

A modular framework combining the expressive power of generative models with physics-assisted learning is proposed to solve inverse problems. The process is iterative, unsupervised, and only requires knowledge of the physical/forward model.

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