全息术
光学
正交基
傅里叶变换
一般化
基函数
空间频率
人工神经网络
基础(线性代数)
频域
计算机科学
计算全息
相(物质)
算法
计算机视觉
人工智能
物理
数学
数学分析
量子力学
几何学
作者
Runze Zhu,Lizhi Chen,Hao Zhang
出处
期刊:Optics Letters
[Optica Publishing Group]
日期:2023-03-27
卷期号:48 (9): 2333-2333
被引量:10
摘要
The use of a deep neural network is a promising technique for rapid hologram generation, where a suitable training dataset is vital for the reconstruct quality as well as the generalization of the model. In this Letter, we propose a deep neural network for phase hologram generation with a physics-informed training strategy based on Fourier basis functions, leading to orthonormal representations of the spatial signals. The spatial frequency characteristics of the reconstructed diffraction fields can be regulated by recombining the Fourier basis functions in the frequency domain. Numerical and optical results demonstrate that the proposed method can effectively improve the generalization of the model with high-quality reconstructions.
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