Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network:: Science Publishing Group

Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network

Jingyuan Ma¹, Guanglin Yang¹, Haiyan Xie²

¹Laboratory of Signal and Information Processing, School of Electronics, Peking University, Beijing, China

²China Science Patent and Trademark Agent, Beijing, China

American Journal of Optics and Photonics, Volume 11, Issue 1, March 2023

Pages: 1-9

Received: Aug. 05, 2023

Accepted: Aug. 28, 2023

Published: Sep. 14, 2023

DOI: 10.11648/j.ajop.20231101.11

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Abstract

In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.

Keywords

Computer Holography, Image Compression, Neural Network, Quantum Computing, Image Reconstruction

To cite this article

Jingyuan Ma, Guanglin Yang, Haiyan Xie. (2023). Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. American Journal of Optics and Photonics, 11(1), 1-9. https://doi.org/10.11648/j.ajop.20231101.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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