Chinese Optics Letters, Volume. 22, Issue 4, 041101(2024)

Non-blind super-resolution reconstruction for laser-induced damage dark-field imaging of optical elements

Qian Wang1, Fengdong Chen1、*, Yueyue Han1, Fa Zeng2、**, Cheng Lu1, and Guodong Liu1、***
Author Affiliations
  • 1Instrument Science and Technology, Harbin Institute of Technology, Harbin 150001, China
  • 2Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China
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    References(12)

    [6] Y. Nan, H. Ji. Deep learning for handling kernel/model uncertainty in image deconvolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2020).

    [8] Z. Wang, X. Cun, J. Bao et al. Uformer: a general U-shaped transformer for image restoration. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2022).

    [9] L. Chen, X. Chu, X. Zhang et al. Simple baselines for image restoration. Computer Vision–ECCV 2022(2022).

    [10] S. Nah, T. H. Kim, K. M. Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 257(2017).

    [11] S.-J. Cho, S. W. Ji, J. P. Hong et al. Rethinking coarse-to-fine approach in single image deblurring. Proceedings of the IEEE/CVF International Conference on Computer Vision(2021).

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    Qian Wang, Fengdong Chen, Yueyue Han, Fa Zeng, Cheng Lu, Guodong Liu, "Non-blind super-resolution reconstruction for laser-induced damage dark-field imaging of optical elements," Chin. Opt. Lett. 22, 041101 (2024)

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    Paper Information

    Category: Imaging Systems and Image Processing

    Received: Oct. 17, 2023

    Accepted: Dec. 11, 2023

    Posted: Dec. 14, 2023

    Published Online: May. 6, 2024

    The Author Email: Fengdong Chen (chenfd@hit.edu.cn), Fa Zeng (cengfa@tsinghua.org.cn), Guodong Liu (lgd@hit.edu.cn)

    DOI:10.3788/COL202422.041101

    CSTR:32184.14.COL202422.041101

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