Hologram Reconstruction using cascaded deep learning networks

Hyon-Gon Choo; Yeon-Gyeong Ju; Kwan-Jung Oh; Yongjun Lim; Jae-Hyeung Park

doi:10.1364/DH.2021.DF4C.3

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DF4C.3
•https://doi.org/10.1364/DH.2021.DF4C.3

Hologram Reconstruction using cascaded deep learning networks

Hyon-Gon Choo, Yeon-Gyeong Ju, Kwan-Jung Oh, Yongjun Lim, and Jae-Hyeung Park

Digital Holography and Three-Dimensional Imaging 2021

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

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Deep Learning, Neural Networks and Holographic Processing II (DF4C)

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H. Choo, Y. Ju, K. Oh, Y. Lim, and J. Park, "Hologram Reconstruction using cascaded deep learning networks," 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 DF4C.3.

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Abstract

Deep learning technology is one of the emerging topics in solving problems in all scientific fields. In this paper, we address a hologram reconstruction method using cascaded multitask networks. A cascaded network consists of two U-net networks. The first is used for conversion between hologram plane and image plane and the other is used for extraction of image and depth. To train the network, we simulate an optical holographic microscopy setup. Experimental results show that the proposed approach can restore effectively complex optical fields and depth information.

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