全息术
光学
计算机科学
计算全息
傅里叶变换
全息显示器
卷积神经网络
物理
数字全息术
相位恢复
人工智能
班级(哲学)
空间频率
相(物质)
量子力学
作者
Ryoichi Horisaki,Yohei Nishizaki,Katsuhisa Kitaguchi,Mamoru Saito,Jun Tanida
出处
期刊:Applied Optics
[The Optical Society]
日期:2021-01-28
卷期号:60 (4): A323-A323
被引量:28
摘要
In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.
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