Chinese Optics, Volume. 17, Issue 4, 842(2024)

Phase gradient estimation using Bayesian neural network

Kang-yang ZHANG1, Zi-hao NI1, Bo DONG1,2, and Yu-lei BAI1,2、*
Author Affiliations
  • 1School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • 2Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing(GDUT), Ministry of Education, Guangzhou 510006, China
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    References(21)

    [2] WANG X D, YUAN X, SHI L P. Optical coherence tomography-in situ and high-speed 3D imaging for laser materials processing[J]. Light:Science & Applications, 11, 280(2022).

    [4] XIE SH L, LIAO W J, BAI Y L. Phase-contrast optical coherence tomography in applications of non-destructive testing[J]. Journal of Guangdong University of Technology, 38, 20-28(2021).

    [7] [7] KENNEDY B F, HILLMAN T R, MCLAUGHLIN R A, et al. In vivo dynamic optical coherence elastography using a ring actuat[J]. Optics Express, 2009, 17(24): 2176221772.

    [16] LIU Z L, LI M Y, LU X Y. On-machine detection technology and application progress of high dynamic range fringe structured light[J]. Chinese Optics, 17, 1-18(2024).

    [19] CHAKRABORTY S, GHOSH M. Applications of Bayesian neural networks in prostate cancer study[J]. Handbook of Statistics, 28, 241-262(2012).

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    Kang-yang ZHANG, Zi-hao NI, Bo DONG, Yu-lei BAI. Phase gradient estimation using Bayesian neural network[J]. Chinese Optics, 2024, 17(4): 842

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    Paper Information

    Received: Sep. 26, 2023

    Accepted: --

    Published Online: Aug. 9, 2024

    The Author Email:

    DOI:10.37188/CO.2023-0168

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