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Holographic Image Reconstruction with Phase Recovery and Autofocusing Using Recurrent Neural Networks

  • Luzhe Huang
    Luzhe Huang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
    More by Luzhe Huang
  • Tairan Liu
    Tairan Liu
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
    More by Tairan Liu
  • Xilin Yang
    Xilin Yang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
    More by Xilin Yang
  • Yi Luo
    Yi Luo
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
    More by Yi Luo
  • Yair Rivenson
    Yair Rivenson
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
  • , and 
  • Aydogan Ozcan*
    Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, United States
    Bioengineering Department, University of California, Los Angeles, California 90095, United States
    California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, United States
    David Geffen School of Medicine, University of California, Los Angeles, California 90095, United States
    *Email: [email protected]
Cite this: ACS Photonics 2021, 8, 6, 1763–1774
Publication Date (Web):May 26, 2021
https://doi.org/10.1021/acsphotonics.1c00337
Copyright © 2021 American Chemical Society

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    Abstract

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    Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information on a hologram is an important step in holographic image reconstruction. Here we demonstrate a convolutional recurrent neural network (RNN) based phase recovery approach that uses multiple holograms, captured at different sample-to-sensor distances, to rapidly reconstruct the phase and amplitude information on a sample while also performing autofocusing through the same network. We demonstrated the success of this deep-learning-enabled holography method by imaging microscopic features of human tissue samples and Papanicolaou (Pap) smears. These results constitute the first demonstration of the use of recurrent neural networks for holographic imaging and phase recovery, and compared with existing methods, the presented approach improves the reconstructed image quality, while also increasing the depth-of-field and inference speed.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsphotonics.1c00337.

    • Supplementary figures of the comparison of the image reconstruction results achieved by RH-M and RH-MD on back-propagated holograms; supplementary tables showing the RH-M reconstruction quality as a function of M and training data set sizes and a quantitative comparison of RH-M and RH-MD image reconstruction results on back-propagated holograms (PDF)

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    Cited By

    This article is cited by 17 publications.

    1. Di Wang, Zhao-Song Li, Yi-Wei Zheng, Nan-Nan Li, Yi-Long Li, Qiong-Hua Wang. High-Quality Holographic 3D Display System Based on Virtual Splicing of Spatial Light Modulator. ACS Photonics 2023, 10 (7) , 2297-2307. https://doi.org/10.1021/acsphotonics.2c01514
    2. Md Sadman Sakib Rahman, Aydogan Ozcan. Computer-Free, All-Optical Reconstruction of Holograms Using Diffractive Networks. ACS Photonics 2021, 8 (11) , 3375-3384. https://doi.org/10.1021/acsphotonics.1c01365
    3. Shuo Wang, Xianan Jiang, Haijun Guo, Huaying Wang. Improved SNR and super-resolution reconstruction of multi-scale digital holography based on deep learning. Optics Communications 2023, 545 , 129634. https://doi.org/10.1016/j.optcom.2023.129634
    4. Ni Chen, Congli Wang, Wolfgang Heidrich. ∂H$\bm{\partial }\mathbf {H}$: Differentiable Holography. Laser & Photonics Reviews 2023, 17 (9) https://doi.org/10.1002/lpor.202200828
    5. Luzhe Huang, Hanlong Chen, Tairan Liu, Aydogan Ozcan. Self-supervised learning of hologram reconstruction using physics consistency. Nature Machine Intelligence 2023, 5 (8) , 895-907. https://doi.org/10.1038/s42256-023-00704-7
    6. Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan. eFIN: Enhanced Fourier Imager Network for Generalizable Autofocusing and Pixel Super-Resolution in Holographic Imaging. IEEE Journal of Selected Topics in Quantum Electronics 2023, 29 (4: Biophotonics) , 1-10. https://doi.org/10.1109/JSTQE.2023.3248684
    7. Min Huang, Bin Zheng, Ruichen Li, Xiaofeng Li, Yijun Zou, Tong Cai, Hongsheng Chen. Diffraction Neural Network for Multi‐Source Information of Arrival Sensing. Laser & Photonics Reviews 2023, 10 https://doi.org/10.1002/lpor.202300202
    8. Vi-hung Tsan, Daniel Fan, Sabina Caneva, Carlas S. Smith, Gerard J. Verbiest. Low-cost acoustic force trap in a microfluidic channel. HardwareX 2023, 14 , e00428. https://doi.org/10.1016/j.ohx.2023.e00428
    9. Xin Tong, Renjun Xu, Ke Liu, Liangliang Zhao, Weilai Zhu, Daomu Zhao. A Deep‐Learning Approach for Low‐Spatial‐Coherence Imaging in Computer‐Generated Holography. Advanced Photonics Research 2023, 4 (1) https://doi.org/10.1002/adpr.202200264
    10. Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan. Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization. Light: Science & Applications 2022, 11 (1) https://doi.org/10.1038/s41377-022-00949-8
    11. Xusheng Zhuang, Aimin Yan, Peter Wai Ming Tsang, Ting-Chung Poon. Deep-learning based reconstruction in optical scanning holography. Optics and Lasers in Engineering 2022, 158 , 107161. https://doi.org/10.1016/j.optlaseng.2022.107161
    12. Yeon-Gyeong Ju, Hyon-Gon Choo, Jae-Hyeung Park. Learning-based complex field recovery from digital hologram with various depth objects. Optics Express 2022, 30 (15) , 26149. https://doi.org/10.1364/OE.461782
    13. Luzhe Huang, Xilin Yang, Tairan Liu, Aydogan Ozcan. Few-shot transfer learning for holographic image reconstruction using a recurrent neural network. APL Photonics 2022, 7 (7) https://doi.org/10.1063/5.0090582
    14. Luzhe Huang, Tairan Liu, Xilin Yang, Yi Luo, Yair Rivenson, Aydogan Ozcan. Phase Recovery and Holographic Imaging using Recurrent Neural Networks (RNNs). 2022, ATh1D.5. https://doi.org/10.1364/CLEO_AT.2022.ATh1D.5
    15. Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan. A broadly generalizable deep neural network for rapid phase recovery and hologram reconstruction. 2022, FM5C.2. https://doi.org/10.1364/FIO.2022.FM5C.2
    16. Guohai Situ. Deep holography. Light: Advanced Manufacturing 2022, 3 (2) , 1. https://doi.org/10.37188/lam.2022.013
    17. Tianjiao Zeng, Yanmin Zhu, Edmund Y. Lam. Deep learning for digital holography: a review. Optics Express 2021, 29 (24) , 40572. https://doi.org/10.1364/OE.443367

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