Photonics Research, Volume. 12, Issue 1, 7(2024)

Learning the imaging mechanism directly from optical microscopy observations

Ze-Hao Wang1,2、†, Long-Kun Shan1,2、†, Tong-Tian Weng1,2, Tian-Long Chen3, Xiang-Dong Chen1,2,4, Zhang-Yang Wang3, Guang-Can Guo1,2,4, and Fang-Wen Sun1,2,4、*
Author Affiliations
  • 1CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
  • 2CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
  • 3University of Texas at Austin, Austin, Texas 78705, USA
  • 4Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
  • show less
    References(57)

    [10] D. Krishnan, T. Tay, R. Fergus. Blind deconvolution using a normalized sparsity measure. Conference on Computer Vision and Pattern Recognition (CVPR), 233-240(2011).

    [12] T. Michaeli, M. Irani. Blind deblurring using internal patch recurrence. European Conference on Computer Vision, 783-798(2014).

    [16] L. Sun, S. Cho, J. Wang. Edge-based blur kernel estimation using patch priors. IEEE International Conference on Computational Photography (ICCP), 1-8(2013).

    [17] Y. Yan, W. Ren, Y. Guo. Image deblurring via extreme channels prior. IEEE Conference on Computer Vision and Pattern Recognition, 4003-4011(2017).

    [22] Z. Wu, Y. Xiong, S. X. Yu. Unsupervised feature learning via non-parametric instance discrimination. IEEE Conference on Computer Vision and Pattern Recognition, 3733-3742(2018).

    [23] K. He, H. Fan, Y. Wu. Momentum contrast for unsupervised visual representation learning. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9729-9738(2020).

    [24] T. Chen, S. Kornblith, M. Norouzi. A simple framework for contrastive learning of visual representations. International Conference on Machine Learning (PMLR), 1597-1607(2020).

    [25] C. Doersch, A. Gupta, A. A. Efros. Unsupervised visual representation learning by context prediction. IEEE International Conference on Computer Vision, 1422-1430(2015).

    [28] T. Chen, S. Liu, S. Chang. Adversarial robustness: from self-supervised pre-training to fine-tuning. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 699-708(2020).

    [29] X. Chen, W. Chen, T. Chen. Self-PU: self boosted and calibrated positive-unlabeled training. International Conference on Machine Learning (PMLR), 1510-1519(2020).

    [30] M. Chen, A. Radford, R. Child. Generative pretraining from pixels. International Conference on Machine Learning (PMLR), 1691-1703(2020).

    [31] O. Henaff. Data-efficient image recognition with contrastive predictive coding. International Conference on Machine Learning (PMLR), 4182-4192(2020).

    [32] D. Pathak, P. Krahenbuhl, J. Donahue. Context encoders: feature learning by inpainting. IEEE Conference on Computer Vision and Pattern Recognition, 2536-2544(2016).

    [36] D. Ulyanov, A. Vedaldi, V. Lempitsky. Deep image prior. IEEE Conference on Computer Vision and Pattern Recognition, 9446-9454(2018).

    [37] T.-Y. Lin, M. Maire, S. Belongie. Microsoft COCO: common objects in context. European Conference on Computer Vision, 740-755(2014).

    [38] L. Liu, H. Jiang, P. He. On the variance of the adaptive learning rate and beyond. 8th International Conference on Learning Representations (ICLR), 1-13(2020).

    [41] A. Makandar, D. Mulimani, M. Jevoor. Comparative study of different noise models and effective filtering techniques. Int. J. Sci. Res., 3, 458-464(2013).

    [47] Y. Zhang, D. Zhou, S. Chen. Single-image crowd counting via multi-column convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition, 589-597(2016).

    [52] Z. Wang, E. P. Simoncelli, A. C. Bovik. Multiscale structural similarity for image quality assessment. 37th Asilomar Conference on Signals, Systems & Computers, 2, 1398-1402(2003).

    [54] O. Ronneberger, P. Fischer, T. Brox. U-NET: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-assisted Intervention, 234-241(2015).

    [55] S. K. Gaire, E. Flowerday, J. Frederick. Deep learning-based spectroscopic single-molecule localization microscopy for simultaneous multicolor imaging. Computational Optical Sensing and Imaging, CTu5F-4(2022).

    Tools

    Get Citation

    Copy Citation Text

    Ze-Hao Wang, Long-Kun Shan, Tong-Tian Weng, Tian-Long Chen, Xiang-Dong Chen, Zhang-Yang Wang, Guang-Can Guo, Fang-Wen Sun. Learning the imaging mechanism directly from optical microscopy observations[J]. Photonics Research, 2024, 12(1): 7

    Download Citation

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

    Category: Image Processing and Image Analysis

    Received: Feb. 22, 2023

    Accepted: Oct. 9, 2023

    Published Online: Dec. 7, 2023

    The Author Email: Fang-Wen Sun (fwsun@ustc.edu.cn)

    DOI:10.1364/PRJ.488310

    Topics