In-line hologram reconstruction with deep learning

Hao Wang; Hao Wang; Meng Lyu; Meng Lyu; Ni Chen; Guohai Situ

doi:10.1364/DH.2018.DW2F.2

Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2018),
paper DW2F.2
•https://doi.org/10.1364/DH.2018.DW2F.2

In-line hologram reconstruction with deep learning

Hao Wang, Meng Lyu, Ni Chen, and Guohai Situ

Digital Holography and Three-Dimensional Imaging 2018

Orlando, Florida United States
25–28 June 2018
ISBN: 978-1-943580-44-6

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Deep Learning in DH (DW2F)

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H. Wang, M. Lyu, N. Chen, and G. Situ, "In-line hologram reconstruction with deep learning," in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), OSA Technical Digest (Optica Publishing Group, 2018), paper DW2F.2.

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Abstract

Deep neural network(DNN) has been applied in many fields. Here, we use DNN to separate interference terms in in-line hologram and reconstruct the pure phase object. The experiment result verifies our method’s feasibility.

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