Two-step Deep Learning for Computational Imaging

Ruibo Shang; Kevin Hoffer-Hawlik; Fei Wang; Fei Wang; Guohai Situ; Guohai Situ; Guohai Situ; Geoffrey P. Luke

doi:10.1364/COSI.2021.CM6B.5

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper CM6B.5
•https://doi.org/10.1364/COSI.2021.CM6B.5

Two-step Deep Learning for Computational Imaging

Ruibo Shang, Kevin Hoffer-Hawlik, Fei Wang, Guohai Situ, and Geoffrey P. Luke

Computational Optical Sensing and Imaging 2021

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

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Machine Learning for Optical Sensing and Imaging (CM6B)

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R. Shang, K. Hoffer-Hawlik, F. Wang, G. Situ, and G. P. Luke, "Two-step Deep Learning for Computational Imaging," 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 CM6B.5.

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Abstract

A two-step deep learning approach is proposed for computational imaging. This approach is robust to image model mismatches since physics priors are not needed and mitigates over-parameterization by training the network in 2 steps.

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