Laser Technology, Volume. 47, Issue 4, 485(2023)

Superresolution reconstruction of holograms based on deep learning

PEI Ruijing1, WANG Shuo1, and WANG Huaying1,2、*
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    References(27)

    [1] [1] ZHANG X, CHEN Zh K, GAO J, et al. A two-stage deep transfer learning model and its application for medical image processing in traditional medicine[J]. Knowledge-Based Systems, 2022, 239(5):108060.

    [2] [2] HOLTKAMP A, ELHENNAWY K, ORO J, et al. Clinical medicine generalizability of deep learning models for caries detection in near-infrared light transillumination images[J]. Journal of Clinical Medicine, 2021, 10(5):961.

    [3] [3] ZHANG Y Y, ZHANG B H, ZHAO Y F, et al. Remote sensing image classification based on dual-channel deep dense feature fusion[J]. Laser Technology, 2021, 45(1):73-79(in Chinese).

    [5] [5] MA G, LLANESl A, IMBERNON B, et al. Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning[J]. The Journal of Supercomputing, 2021,77(9):1-23.

    [6] [6] WU Y F, WU J C, HAO R, et al. Research progress of particle field digital holography based on deep learning[J]. Journal of Applied Optics,2020,41(4):662-674.

    [7] [7] XIANG D, GUI J B, LIU Ch, et al. Experiment research of accurate wavefront reconstruction of digital holography[J]. Laser Technology, 2017, 41(3): 406-410(in Chinese).

    [9] [9] LI F Q, ZHAO M, TIAN Z M, et al. Compressive ghost imaging through scattering media with deep learning[J]. Optics Express,2020,28(12):17395-17408.

    [10] [10] YIN D, GU Z Z, ZHANG Y R. Digital holographic reconstruction based on deep learning framework with unpaired data[J]. IEEE Photonics Journal,2020,12(2):1-12.

    [11] [11] WU Y C, RIVENSON Y, ZHANG Y B, et al. Extended depth-of-field in holographic imaging using deeplearning-based autofocusing and phase recovery[J]. Optica,2018, 5(6):704-710.

    [12] [12] REN Zh B, XU Zh M, LAM E Y M. End-to-end deep learning framework for digital holographic reconstruction[J]. Advanced Photonics, 2019,1(1):016004.

    [13] [13] WANG H, LYU M, SITU G. eHoloNet: A learning-based end-to-end approach for in-line digital holographic reconstruction[J]. Optics Express, 2018, 26(18): 22603-22614.

    [14] [14] WANG K Q, DOU J Z, QIAN K M, et al. Y-Net: A one-to-two deep learning framework for digital holographic reconstruction[J]. Optics Letters,2019,44(19):4765-4768.

    [15] [15] LI J, LI J. Research on single hologram reconstruction method based on U-Net network[J]. Laser Journal,2020,41(1):96-99(in Chin-ese).

    [16] [16] XIAO W, LI J, PAN F, et al. Super-resolution in digital holographic phase cell image based on usenet[J]. Acta Photonica Sinica,2020,49(6):173-184(in Chinese).

    [17] [17] PIRONE D, SIRICO D, MICCIO L, et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning[J]. Lab on a Chip,2022,22(4):793-804.

    [18] [18] NAYFE B H, ABDULLAH S N H S, SULAIMAN R, et al. Optimized leakyrelu for handwritten arabic character recognition using convolution neural networks[J]. Multimedia Tools and Applications, 2022, 81(2):2065-2094.

    [19] [19] LUI F F. Investigation of exact phase reconstruction algorithm and expeiments in digital holographic microscopy[D].Handan: Hebei University of Engineering,2014:42-124(in Chinese).

    [20] [20] SONG X F, YU M J, WANG H Y, et al. Effect of reference intensity ratio to object on reconstructed image quality in digital holography[J]. Laser Technology, 2014, 38(6): 859-862(in Chinese).

    [22] [22] LIU H, LI D, JIANG B, et al. Mgbn-yolo: A faster light-weight object detection model for robotic grasping ofbolster spring based on image-based visual servoing[J]. Journal of Intelligent & Robotic Systems, 2022,104(4):77.

    [23] [23] WEI B, SHEN X, YUAN Y. Remote sensing scene classification based on improved ghostnet[J]. Journal of Physics Conference Series, 2020, 1621(1):012091.

    [24] [24] WEI Y, YUAN Q, SHEN H, et al. Boosting the accuracy of multispectral image pansharpening by learning a deep residual network[J]. Geoscience and Remote Sensing Letters,2017,14(10):1795-1799.

    [25] [25] JIANG Y, CHEN L, ZHANG H, et al. Breast cancer histopathological image classification using convolutional neural networks with small se-resnet module[J]. Plos One, 2019, 14(3):10214587.

    [26] [26] MAO Zh R, DU Y Ch, XIAO Sh B, et al. Fine-grained image classification method based on ECA-net and multi-scale[J]. Application Research of Computers,2021,38(11):3484-3488(in Chinese).

    [27] [27] XUE H, SUN M H, LIANG Y H. ECANet: Explicit cyclic attention-based network for video saliency prediction[J]. Neurocomputing,2022,468(C):233-244.

    Tools

    Get Citation

    Copy Citation Text

    PEI Ruijing, WANG Shuo, WANG Huaying. Superresolution reconstruction of holograms based on deep learning[J]. Laser Technology, 2023, 47(4): 485

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Apr. 20, 2022

    Accepted: --

    Published Online: Dec. 11, 2023

    The Author Email: WANG Huaying (pbxsyingzi@126.com)

    DOI:10.7510/jgjs.issn.1001-3806.2023.04.007

    Topics