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

Mengjia Liu; Guanglin Yang; Haiyan Xie

doi:10.1117/1.OE.56.2.023104

21 February 2017 Method of computer-generated hologram compression and transmission using quantum back-propagation neural network

Mengjia Liu, Guanglin Yang, Haiyan Xie

Author Affiliations +

Optical Engineering, Vol. 56, Issue 2, 023104 (February 2017). https://doi.org/10.1117/1.OE.56.2.023104

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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Mengjia Liu, Guanglin Yang, and Haiyan Xie "Method of computer-generated hologram compression and transmission using quantum back-propagation neural network," Optical Engineering 56(2), 023104 (21 February 2017). https://doi.org/10.1117/1.OE.56.2.023104

Received: 11 October 2016; Accepted: 7 February 2017; Published: 21 February 2017

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