卷积误差消除的模型驱动相位型全息图生成网络

何泽浩; 刘珂瑄; 曹良才; 张岩

doi:10.3981/j.issn.1000-7857.2024.12.01771

科技导报 >

2025 , Vol. 43 >Issue 5: 107 - 116

DOI: https://doi.org/10.3981/j.issn.1000-7857.2024.12.01771

研究论文

卷积误差消除的模型驱动相位型全息图生成网络

何泽浩 ^,¹ ,
刘珂瑄 ² ,
曹良才 ^,²^,* ,
张岩 ^,¹^,*

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1. 首都师范大学物理系, 北京 100048
2. 清华大学精密仪器系, 北京 100084

曹良才（通信作者），教授，研究方向为全息光学，电子信箱：clc@tsinghua.edu.cn

张岩（共同通信作者），教授，研究方向为太赫兹光电子学、微纳光学、光学信息处理，电子信箱：yzhang@cnu.edu.cn

何泽浩，讲师，研究方向为全息显示和通信，电子信箱：hezehao@cnu.edu.cn

收稿日期: 2024-12-22

网络出版日期: 2025-04-11

基金资助

国家自然科学基金青年科学基金项目(62205173)

国家自然科学基金专项项目(62441613)

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Convolution-error-free model-driven neural network for phase-only computer-generated hologram

Zehao HE ^,¹ ,
Kexuan LIU ² ,
Liangcai CAO ^,²^,* ,
Yan ZHANG ^,¹^,*

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1. Department of Physics, Capital Normal University, Beijing 100048, China
2. Department of Precision Instrument, Tsinghua University, Beijing 100084, China

Received date: 2024-12-22

Online published: 2025-04-11

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摘要

计算全息技术是动态三维显示的理想解决方案，具有广阔的应用前景。在当前的技术条件下，计算全息技术面临的最大挑战是全息算法难以同时兼顾计算的精度和速度。为此，提出了卷积误差消除的模型驱动相位型全息图生成网络，实现了高保真相位型全息图的快速生成。首先，研究了角谱法中卷积误差的产生机制，提出了无卷积误差的角谱法，开发了基于无卷积误差角谱法的迭代框架，证实了无卷积误差角谱法对于提升相位型全息图计算精度的有效性；其次，以无卷积误差角谱法作为编码器构建了卷积误差消除的模型驱动相位型全息图生成网络，将相位型全息图的计算时间减小了3个数量级。通过网络生成的相位型全息图，抑制了全息光学重建中的散斑噪声，提高了重建结果的细节质量，平均峰值信噪比高达20.38 dB。伴随着深度梯度显著性和通道效率一致性的继续提升，该网络有望广泛应用在虚拟现实、元宇宙和三维视频通讯等领域。

关键词： 计算全息; 三维显示; 深度学习; 神经网络; 卷积误差

本文引用格式

何泽浩 , 刘珂瑄 , 曹良才 , 张岩 . 卷积误差消除的模型驱动相位型全息图生成网络[J]. 科技导报, 2025 , 43(5) : 107 -116 . DOI: 10.3981/j.issn.1000-7857.2024.12.01771

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

1	曹良才, 何泽浩, 刘珂瑄, 等. 元宇宙中的动态全息三维显示: 发展与挑战(特邀)[J]. 红外与激光工程, 2022, 51(1): 267- 281.

2	何泽浩, 曹良才. 面向沉浸式元宇宙的显示、交互和应用[J]. 科技导报, 2023, 41(5): 6- 14.

3	Blundell B , Schwarz A . Volumetric three dimensional display system[M]. New York: Wiley-IEEE Press, 1999.

4	Okoshi T . Three-dimensional imaging techniques[M]. New York: Academic Press, 1976.

5	Goodman J P . Introduction to Fourier optics[M]. 4th ed New York: W. H. Freeman&Company, 2017.

6	Li C , Cao L , Wang Z , et al. Hybrid polarization-angle multiplexing for volume holography in gold nanoparticle-doped photopolymer[J]. Optics Letters, 2014, 39(24): 6891- 6894. DOI

7	Sui X , He Z , Chu D , et al. Non-convex optimization for inverse problem solving in computer-generated holography[J]. Light: Science&Applications, 2024, 13: 158.

8	Mengu D , Ulusoy E , Urey H . Non-iterative phase hologram computation for low speckle holographic image projection[J]. Optics Express, 2016, 24(5): 4462- 4476. DOI

9	Zea A V , Torroba R . Optimized random phase tiles for noniterative hologram generation[J]. Applied Optics, 2019, 58(32): 9013- 9019. DOI

10	Zhao T , Liu J , Duan J , et al. Image quality enhancement via gradient-limited random phase addition in holographic display[J]. Optics Communications, 2019, 442: 84- 89. DOI

11	Chen C-Y , Chang H T , Chang T-J , et al. Full-color and lessspeckled modified Gerchberg-Saxton algorithm computergenerated hologram floating in a dual-parabolic projection system[J]. Chinese Optics Letters, 2015, 13(11): 110901. DOI

12	Liu K , He Z , Cao L . Double amplitude freedom GerchbergSaxton algorithm for generation of phase-only hologram with speckle suppression[J]. Applied Physical Letters, 2021, 120(6): 061103.

13	Yan X , Liu X , Li J , et al. Generating multi-depth 3D holograms using a fully convolutional neural network[J]. Advanced Science, 2024, 11(28): 2308886. DOI

14	Liu K , Wu J , He Z , et al. 4K-DMDNet: Diffraction modeldriven network for 4K computer-generated holography[J]. Opto-Electronic Advances, 2023, 6(5): 220135. DOI

15	Chang C , Wang D , Zhu D , et al. Deep-learning-based computer-generated hologram from a stereo image pair[J]. Optics Letters, 2022, 47(6): 1482- 1485. DOI

16	Lee M H , Lew H M , Youn S , et al. Deep learning-based framework for fast and accurate acoustic hologram generation[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022, 69(12): 3353- 3366. DOI

17	Kozacki T , Martinez-Carranza J , Kukolowicz R , et al. Accurate reconstruction of horizontal parallax-only holograms by angular spectrum and efficient zero-padding[J]. Applied Optics, 2020, 59(27): 8450- 8458. DOI

18	He Z , Sui X , Jin G , et al. Distortion correction method based on angular spectrum algorithm for holographic display[J]. IEEE Transactions on Industrial Informatics, 2019, 15(11): 6162- 6169. DOI

19	Matsushima K , Shimobaba T . Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields[J]. Optics Express, 2009, 17(22): 19662- 19673. DOI

20	Williamson D . Discrete-time signal processing[M]. New York: Springer, 1999.

21	He Z , Sui X , Jin G , et al. Optimal quantization for amplitude and phase in computer-generated holography[J]. Optics Express, 2021, 29(1): 119- 133. DOI

22	Wang X , He Z , Cao L . Analysis of reconstruction quality for computer-generated holograms using a model free of circular-convolution error[J]. Optics Express, 2023, 31(12): 19021- 19035. DOI

23	Shi W, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 1874-1883.

24	Wu J , Liu K , Sui X , et al. High-speed computer-generated holography using an autoencoder-based deep neural network[J]. Optics Letters, 2021, 46(12): 2908- 2911. DOI

25	Velez-Zea A , Torroba R . Mixed constraint in global and sequential hologram generation[J]. Applied Optics, 2021, 60(7): 1888- 1895. DOI

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