Laser & Optoelectronics Progress, Volume. 59, Issue 6, 0617026(2022)

Automatic Phase Recognition Method Based on Convolutional Neural Network

Ying Ji1、*, Lingran Gong1, Shuang Fu2, and Yawei Wang1
Author Affiliations
  • 1School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang , Jiangsu 212013, China
  • 2Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen , Guangdong 518055, China
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    References(27)

    [6] Wang F, Wang H, Bian Y M et al. Applications of deep learning in computational imaging[J]. Acta Optica Sinica, 40, 0111002(2020).

    [7] Zuo C, Feng S J, Zhang X Y et al. Deep learning based computational imaging: status, challenges, and future[J]. Acta Optica Sinica, 40, 0111003(2020).

    [14] Wang P, Liu R, Xin X J et al. Scene classification of optical remote sensing images based on residual networks[J]. Laser & Optoelectronics Progress, 58, 0210001(2021).

    [21] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014).

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    Ying Ji, Lingran Gong, Shuang Fu, Yawei Wang. Automatic Phase Recognition Method Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2022, 59(6): 0617026

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    Paper Information

    Category: Medical Optics and Biotechnology

    Received: Jun. 28, 2021

    Accepted: Aug. 31, 2021

    Published Online: Mar. 8, 2022

    The Author Email: Ying Ji (jy@ujs.edu.cn)

    DOI:10.3788/LOP202259.0617026

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