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Appl. Sci. 2018, 8(8), 1258; https://doi.org/10.3390/app8081258

Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

1,2,3,†
,
1,2,†
,
4
,
1,2
,
1,2,* and 1
1
Shenzhen Key Lab of Advanced Telecommunication and Information Processing, College of Information Engineering, Shenzhen University, Shenzhen 518060, China
2
Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen 518060, China
3
Tsinghua Berkeley Shenzhen Institute (TBSI), Shenzhen 518000, China
4
Jiangsu Key Laboratory for Opto-Electronic Technology, School of Physics and Technology, Nanjing Normal University, WenYuan Road 1, Nanjing 210023, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 22 June 2018 / Revised: 22 July 2018 / Accepted: 24 July 2018 / Published: 30 July 2018
(This article belongs to the Special Issue Holography, 3D Imaging and 3D Display)
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Abstract

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression. View Full-Text
Keywords: hologram; holography; phase-only; compression; deep learning; JPEG; convolutional neural network hologram; holography; phase-only; compression; deep learning; JPEG; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Jiao, S.; Jin, Z.; Chang, C.; Zhou, C.; Zou, W.; Li, X. Compression of Phase-Only Holograms with JPEG Standard and Deep Learning. Appl. Sci. 2018, 8, 1258.

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