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Optical Engineering • new

Method of computer-generated hologram compression and transmission using quantum back-propagation neural network
Author(s): Mengjia Liu; Guanglin Yang; Haiyan Xie
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Paper Abstract

A method for computer-generated hologram (CGH) compression and transmission using a quantum back-propagation neural network (QBPNN) is proposed, with the Fresnel transform technique adopted for image reconstruction of the compressed and transmitted CGH. Experiments of simulation were conducted to compare the reconstructed images from CGHs processed using a QBPNN with those processed using a back-propagation neural network (BPNN) at the optimal learning coefficients. The experimental results show that the method using a QBPNN could produce reconstructed images with a better quality than those obtained using a BPNN despite the use of fewer learning iterations at the same compression ratio.

Paper Details

Date Published: 21 February 2017
PDF: 6 pages
Opt. Eng. 56(2) 023104 doi: 10.1117/1.OE.56.2.023104
Published in: Optical Engineering Volume 56, Issue 2
Show Author Affiliations
Mengjia Liu, Peking Univ. (China)
Guanglin Yang, Peking Univ. (China)
Haiyan Xie, China Science Patent & Trademark Agent Ltd. (China)

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