Imaging Components, Systems, and Processing

Method of computer-generated hologram compression and transmission using quantum back-propagation neural network

[+] Author Affiliations
Mengjia Liu, Guanglin Yang

Peking University, Laboratory of Signal and Information Processing, Department of Electronics, School of Electronic Engineering and Computer Science, Beijing, China

Haiyan Xie

China Science Patent Trademark Agents Ltd., Beijing, China

Opt. Eng. 56(2), 023104 (Feb 21, 2017). doi:10.1117/1.OE.56.2.023104
History: Received October 11, 2016; Accepted February 7, 2017
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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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Mengjia Liu ; Guanglin Yang and Haiyan Xie
"Method of computer-generated hologram compression and transmission using quantum back-propagation neural network", Opt. Eng. 56(2), 023104 (Feb 21, 2017). ; http://dx.doi.org/10.1117/1.OE.56.2.023104


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