Phase Retrieval for Terahertz Holography with Physics-Informed Deep Learning

Mingjun Xiang; Mingjun Xiang; Mingjun Xiang; Mingjun Xiang; Lingxiao Wang; Lingxiao Wang; Yu Sha; Yu Sha; Yu Sha; Hui Yuan; Kai Zhou; Hartmut G. Roskos

doi:10.1364/DH.2022.Tu4A.4

Digital Holography and 3-D Imaging 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper Tu4A.4
•https://doi.org/10.1364/DH.2022.Tu4A.4

Phase Retrieval for Terahertz Holography with Physics-Informed Deep Learning

Mingjun Xiang, Lingxiao Wang, Yu Sha, Hui Yuan, Kai Zhou, and Hartmut G. Roskos

Digital Holography and Three-Dimensional Imaging 2022

Cambridge United Kingdom
1–4 August 2022
ISBN: 978-1-957171-12-8

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Phase Processing, Retrieval Techniques, and Applications I (Tu4A)

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M. Xiang, L. Wang, Y. Sha, H. Yuan, K. Zhou, and H. G. Roskos, "Phase Retrieval for Terahertz Holography with Physics-Informed Deep Learning," in Digital Holography and 3-D Imaging 2022, D. Chu, J. Park, C. Cheng, and P. Ferraro, eds., Technical Digest Series (Optica Publishing Group, 2022), paper Tu4A.4.

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Abstract

Two novel phase retrieval methods for THz holography based on physics-informed deep learning are presented. They employ unsupervised learning and supervised learning based on the MNIST dataset, respectively.

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