Journal of Applied Optics, Volume. 44, Issue 2, 314(2023)

Electroluminescence image enhancement technology of half-cut photovoltaic module based on DCGANs

Xiang HE1,2、*
Author Affiliations
  • 1National PV Industry Measurement and Testing Center, Fujian Metrology Institute, Fuzhou 350003 China
  • 2Fujian Key Laboratory of Energy Measurement, Fuzhou 350003, China
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    References(5)

    [2] [2] ZHOU Ying, MAO Li, ZHANG Yan, et al. Research on defect detection and classification for solar cells based on improved convolutional neural network[J]. Acta Energiae Solaris Sinica, 2020, 41(12): 69-76.

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    [14] [14] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention. [S. l. ]: [s. n. ], 2015: 234-241.

    [15] [15] RADFORD A, METZ L, CHINTALA S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. (2022-05-10)[2016-01-17].https://arxiv.org/abs/1511.06434.

    [16] [16] WANG Z, SIMONCELLI E P, BOVIK A C. Multi-scale structural similarity for image quality assessment[C]//Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Nov 9-12, 2003. Piscataway: IEEE, 2003: 1398-1402.

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    Xiang HE. Electroluminescence image enhancement technology of half-cut photovoltaic module based on DCGANs[J]. Journal of Applied Optics, 2023, 44(2): 314

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    Paper Information

    Category: Research Articles

    Received: May. 16, 2022

    Accepted: --

    Published Online: Apr. 14, 2023

    The Author Email:

    DOI:10.5768/JAO202344.0202003

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