Photonics Research, Volume. 12, Issue 3, 474(2024)

Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging On the Cover

Chang Qiao1,2、†, Haoyu Chen3,4、†, Run Wang1、†, Tao Jiang3,4, Yuwang Wang5,6, and Dong Li3,4、*
Author Affiliations
  • 1Department of Automation, Tsinghua University, Beijing 100084, China
  • 2Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China
  • 3National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
  • 4College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  • 5Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
  • 6e-mail: wang-yuwang@mail.tsinghua.edu.cn
  • show less
    References(24)

    [10] Y. Zhang, K. P. Li, K. Li. Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), 294-310(2018).

    [11] O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 1-8(2015).

    [12] J. Gu, H. Lu, W. Zuo. Blind super-resolution with iterative kernel correction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1604-1613(2019).

    [14] J. Caballero, C. Ledig, A. Aitken. Real-time video super-resolution with spatio-temporal networks and motion compensation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4778-4787(2017).

    [17] J. Liang, G. Sun, K. Zhang. Mutual affine network for spatially variant kernel estimation in blind image super-resolution. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4096-4105(2021).

    [19] D. Ren, K. Zhang, Q. Wang. Neural blind deconvolution using deep priors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3341-3350(2020).

    [20] J. Liang, K. Zhang, S. Gu. Flow-based kernel prior with application to blind super-resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10601-10610(2021).

    [23] Z. Liu, Y. Lin, Y. Cao. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, 10012-10022(2021).

    Tools

    Get Citation

    Copy Citation Text

    Chang Qiao, Haoyu Chen, Run Wang, Tao Jiang, Yuwang Wang, Dong Li. Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging[J]. Photonics Research, 2024, 12(3): 474

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Imaging Systems, Microscopy, and Displays

    Received: Sep. 27, 2023

    Accepted: Dec. 20, 2023

    Published Online: Feb. 29, 2024

    The Author Email: Dong Li (lidong@ibp.ac.cn)

    DOI:10.1364/PRJ.506778

    Topics