High-speed computer-generated holography using an autoencoder-based deep neural network

Wu Jiachen; Liu Kexuan; Sui Xiaomeng; Cao Liangcai

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High-speed computer-generated holography using an autoencoder-based deep neural network

机译：使用基于AutoEncoder的深神经网络的高速计算机生成的全息术

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Learning-based computer-generated holography (CGH) provides a rapid hologram generation approach for holographic displays. Supervised training requires a large-scale dataset with target images and corresponding holograms. We propose an autoencoder-based neural network (holoencoder) for phase-only hologram generation. Physical diffraction propagation was incorporated into the autoencoder's decoding part. The holoencoder can automatically learn the latent encodings of phase-only holograms in an unsupervised manner. The proposed holoencoder was able to generate high-fidelity 4K resolution holograms in 0.15 s. The reconstruction results validate the good generalizability of the holoencoder, and the experiments show fewer speckles in the reconstructed image compared with the existing CGH algorithms. (C) 2021 Optical Society of America.

机译：基于学习的计算全息（CGH）为全息显示提供了一种快速的全息图生成方法。监督训练需要一个包含目标图像和相应全息图的大规模数据集。我们提出了一种基于自动编码器的神经网络（holoencoder），用于相位全息图的生成。自动编码器的解码部分包含了物理衍射传播。全息编码器可以在无监督的情况下自动学习纯相位全息图的潜在编码。该全息编码器能够在0.15s内生成高保真4K分辨率的全息图。重建结果验证了全息编码器的良好通用性，实验表明，与现有的CGH算法相比，重建图像中的斑点更少。（2021）美国光学学会。

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来源
《Optics Letters》 |2021年第12期|共4页
作者
Wu Jiachen; Liu Kexuan; Sui Xiaomeng; Cao Liangcai;
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作者单位

Tsinghua Univ Dept Precis Instruments State Key Lab Precis Measurement Technol &

Instru Beijing 100084 Peoples R China;

Tsinghua Univ Dept Precis Instruments State Key Lab Precis Measurement Technol &

Instru Beijing 100084 Peoples R China;

Tsinghua Univ Dept Precis Instruments State Key Lab Precis Measurement Technol &

Instru Beijing 100084 Peoples R China;

Tsinghua Univ Dept Precis Instruments State Key Lab Precis Measurement Technol &

Instru Beijing 100084 Peoples R China;

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正文语种 eng
中图分类计量学;光学;
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机译：人工神经网络中的全息术

High-speed computer-generated holography using an autoencoder-based deep neural network

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