Optimized initial weight in quantum-inspired neural network for compressing computer-generated holograms

Shenhua Hou; Guanglin Yang; Haiyan Xie

doi:10.1117/1.OE.58.5.053105

24 May 2019 Optimized initial weight in quantum-inspired neural network for compressing computer-generated holograms

Shenhua Hou, Guanglin Yang, Haiyan Xie

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Science Foundation of China

Optical Engineering, Vol. 58, Issue 5, 053105 (May 2019). https://doi.org/10.1117/1.OE.58.5.053105

Abstract

We propose an optimized initial weight scheme in a quantum-inspired neural network for compressing computer-generated holograms (CGHs). An optimized initial weight generation strategy is applied to accelerate the compression process. The pixel blocks’ complexity distribution of CGH is analyzed, and the parallel quantum neural network structure is used to compress the image pixel blocks. A deep convolutional neural network with residual learning is also adopted for improving the quality of the reconstructed image. The experimental results have shown that the compression iterations are reduced by using the optimized initial weight, and the reconstructed image quality of the compressed CGH is improved using the parallel quantum-inspired neural network structure and the deep convolutional neural network with residual learning.

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Shenhua Hou, Guanglin Yang, and Haiyan Xie "Optimized initial weight in quantum-inspired neural network for compressing computer-generated holograms," Optical Engineering 58(5), 053105 (24 May 2019). https://doi.org/10.1117/1.OE.58.5.053105

Received: 10 January 2019; Accepted: 30 April 2019; Published: 24 May 2019

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KEYWORDS

Computer generated holography

Image compression

Image quality

Neural networks

Neurons

3D image reconstruction

Chromium

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