Researching | DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding

Advanced Imaging, Volume. 2, Issue 5, 051004(2025)

DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding

Shu-Feng Lin, Jingwei Chen, Dayong Wang, Jie Zhao, Lu Rong, Yunxin Wang, Yu Zhao^*, and Chao Ping Chen^*

Author Affiliations

show less

Full Text Get PDF

Figures&Tables (11)

References (26)

Paper Information

References(26)

[1] P. A. Blanche. Holography, and the future of 3D display. Light Adv. Manuf., 2, 446(2021). https://doi.org/10.37188/lam.2021.028

[2] C. P. Chen, Y. Su, C. G. Jhun. Recent advances in holographic recording media for dynamic holographic display. J. Opt. Photonics, 1, 1(2014). https://doi.org/10.7243/2057-1569-1-1

[3] Y. Xiong et al. Coherent backlight system for flat-panel holographic 3D display. Opt. Commun., 296, 41(2013). https://doi.org/10.1016/j.optcom.2013.01.045

[4] L. Zhou et al. See-through near-eye displays enabling vision correction. Opt. Express, 25, 2130(2017). https://doi.org/10.1364/OE.25.002130

[5] W. Ouyang et al. Ultrafast 3D nanofabrication via digital holography. Nat. Commun., 14, 1716(2023). https://doi.org/10.1038/s41467-023-37163-y

[6] T. Nishitsuji et al. Review of fast calculation techniques for computer-generated holograms with the point-light-source-based model. IEEE Trans. Ind. Inf., 13, 2447(2017). https://doi.org/10.1109/TII.2017.2669200

[7] P. W. M. Tsang et al. Review of fast methods for point-based computer-generated holography. Photonics Res., 6, 837(2018). https://doi.org/10.1364/PRJ.6.000837

[8] H. Pang et al. Accurate hologram generation using layer-based method and iterative Fourier transform algorithm. IEEE Photonics J., 8, 1(2016). https://doi.org/10.1109/JPHOT.2016.2574333

[9] B. R. Brown, A. W. Lohmann. Complex spatial filtering with binary masks. Appl. Opt., 5, 967(1966). https://doi.org/10.1364/AO.5.000967

[10] H. Ko, H. Y. Kim. Deep learning-based compression for phase-only hologram. IEEE Access, 9, 79735(2021). https://doi.org/10.1109/ACCESS.2021.3084800

[11] Y. Wu et al. Generation of phase-only Fourier hologram based on double phase method and quantization error analysis. IEEE Photonics J., 12, 7000913(2020). https://doi.org/10.1109/JPHOT.2020.3017503

[12] K. Liu, Z. He, L. Cao. Pattern-adaptive error diffusion algorithm for improved phase-only hologram generation. Chin. Opt. Lett., 19, 050501(2021). https://doi.org/10.3788/COL202119.050501

[13] Y. Wu et al. Adaptive weighted Gerchberg-Saxton algorithm for generation of phase-only hologram with artifacts suppression. Opt. Express, 29, 1412(2021). https://doi.org/10.1364/OE.413723

[14] S. Liu, Y. Takaki. Optimization of phase-only computer-generated holograms based on the gradient descent method. Appl. Sci., 10, 4283(2020). https://doi.org/10.3390/app10124283

[15] P. Chakravarthula et al. Wirtinger holography for near-eye displays. ACM T. Graphic., 38, 213(2019). https://doi.org/10.1145/3355089.3356539

[16] Y. Wang et al. Hardware implementations of computer-generated holography: a review. Opt. Eng., 59, 102413(2020). https://doi.org/10.1117/1.OE.59.10.102413

[17] R. Horisaki, R. Takagi, J. Tanida. Deep-learning-generated holography. Appl. Opt., 57, 3859(2018). https://doi.org/10.1364/AO.57.003859

[18] Y. Peng et al. Neural holography with camera-in-the-loop training. ACM. T. Graphic., 39, 1(2020). https://doi.org/10.1145/3414685.3417802

[19] L. Shi et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature, 591, 234(2021). https://doi.org/10.1038/s41586-020-03152-0

[20] J. Wu et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt. Lett., 46, 2908(2021). https://doi.org/10.1364/OL.425485

[21] C. Zhong et al. Real-time high-quality computer-generated hologram using complex-valued convolutional neural network. IEEE Trans. Vis. Comput. Graph., 30, 3709(2023). https://doi.org/10.1109/TVCG.2023.3239670

[22] J. Lee et al. Deep neural network for multi-depth hologram generation and its training strategy. Opt. Express, 28, 27137(2020). https://doi.org/10.1364/OE.402317

[23] C. Zhong et al. Real-time 4K computer-generated hologram based on encoding conventional neural network with learned layered phase. Sci. Rep., 13, 19372(2023). https://doi.org/10.1038/s41598-023-46575-1

[24] X. Yan et al. Generation of multiple-depth 3D computer-generated holograms from 2D-image-datasets trained CNN. Adv. Sci., 12, 2408610(2024). https://doi.org/10.1002/advs.202408610

[25] X. Yan et al. Recoding double-phase holograms with the full convolutional neural network. Opt. Laser Technol., 174, 110667(2024). https://doi.org/10.1016/j.optlastec.2024.110667

[26] Y. Zhao et al. Holographic voice-interactive system with Taylor Rayleigh-Sommerfeld based point cloud gridding. Opt. Laser Eng., 179, 108270(2024). https://doi.org/10.1016/j.optlaseng.2024.108270

Tools

Get Citation

Copy Citation Text

Shu-Feng Lin, Jingwei Chen, Dayong Wang, Jie Zhao, Lu Rong, Yunxin Wang, Yu Zhao, Chao Ping Chen, "DWS-Net: a depth-wise separable convolutional neural network for robust phase-only hologram encoding," Adv. Imaging 2, 051004 (2025)

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Research Article

Received: May. 27, 2025

Accepted: Aug. 14, 2025

Published Online: Sep. 23, 2025

The Author Email: Yu Zhao (zhaoyu@yzu.edu.cn), Chao Ping Chen (ccp@sjtu.edu.cn)

DOI:10.3788/AI.2025.10012

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology