MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution

Richard W. Shuai; Kyrollos Yanny; Kristina Monakhova; Laura Waller

doi:10.1364/COSI.2021.CTh5A.5

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper CTh5A.5
•https://doi.org/10.1364/COSI.2021.CTh5A.5

MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution

Richard W. Shuai, Kyrollos Yanny, Kristina Monakhova, and Laura Waller

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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R. W. Shuai, K. Yanny, K. Monakhova, and L. Waller, "MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution," 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.5.

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Abstract

We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experimental data, demonstrating high resolution reconstructions.

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