High Power Laser and Particle Beams, Volume. 34, Issue 6, 064002(2022)

Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network

Dong Li, Liang Sheng, Yang Li, and Baojun Duan
Author Affiliations
  • State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
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    References(8)

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    [5] [5] Simonyan K, Zisserman A. Very deep convolutional wks f largescale image recognition[J]. Computer Science, 2014.

    [6] [6] He K , Zhang X , Ren S , et al. Deep residual learning f image recognition[C]2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). 2016: 770778.

    [7] [7] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[M]. IEEE Computer Society, 2014.

    [8] Kai Z, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2016).

    [10] Pan S J, Qiang Y. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

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    Dong Li, Liang Sheng, Yang Li, Baojun Duan. Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network[J]. High Power Laser and Particle Beams, 2022, 34(6): 064002

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    Paper Information

    Category: Particle Beams and Accelerator Technology

    Received: Aug. 9, 2021

    Accepted: --

    Published Online: Jun. 2, 2022

    The Author Email:

    DOI:10.11884/HPLPB202234.210345

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