• Advanced Photonics
  • Vol. 5, Issue 2, 026003 (2023)
Yilin He1、†, Yunhua Yao1, Dalong Qi1, Yu He1, Zhengqi Huang1, Pengpeng Ding1, Chengzhi Jin1, Chonglei Zhang2, Lianzhong Deng1, Kebin Shi3, Zhenrong Sun1, Xiaocong Yuan2、*, and Shian Zhang1、4、*
Author Affiliations
  • 1East China Normal University, School of Physics and Electronic Science, State Key Laboratory of Precision Spectroscopy, Shanghai, China
  • 2Shenzhen University, Institute of Microscale Optoelectronics, Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen, China
  • 3Peking University, School of Physics, Frontiers Science Center for Nanooptoelectronics, State Key Laboratory for Mesoscopic Physics, Beijing, China
  • 4Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China
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    DOI: 10.1117/1.AP.5.2.026003 Cite this Article Set citation alerts
    Yilin He, Yunhua Yao, Dalong Qi, Yu He, Zhengqi Huang, Pengpeng Ding, Chengzhi Jin, Chonglei Zhang, Lianzhong Deng, Kebin Shi, Zhenrong Sun, Xiaocong Yuan, Shian Zhang. Temporal compressive super-resolution microscopy at frame rate of 1200 frames per second and spatial resolution of 100 nm[J]. Advanced Photonics, 2023, 5(2): 026003 Copy Citation Text show less
    References

    [1] S. W. Hell, J. Wichmann. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy. Opt. Lett., 19, 780-782(1994).

    [2] S. Manley et al. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods, 5, 155-157(2008).

    [3] M. J. Rust, M. Bates, X. W. Zhuang. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods, 3, 793-795(2006).

    [4] M. G. L. Gustafsson. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc., 198, 82-87(2000).

    [5] T. Dertinger et al. Superresolution optical fluctuation imaging with organic dyes. Angew. Chem. Int. Ed., 49, 9441-9443(2010).

    [6] M. Weber et al. MINSTED fluorescence localization and nanoscopy. Nat. Photonics, 15, 361-366(2021).

    [7] F. Balzarotti et al. Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes. Science, 355, 606-612(2017).

    [8] H. Zhang, M. Zhao, L. L. Peng. Nonlinear structured illumination microscopy by surface plasmon enhanced stimulated emission depletion. Opt. Express, 19, 24783-24794(2011).

    [9] L. Reymond et al. SIMPLE: structured illumination based point localization estimator with enhanced precision. Opt. Express, 27, 24578-24590(2019).

    [10] L. Schermelleh et al. Super-resolution microscopy demystified. Nat. Cell Biol., 21, 72-84(2019).

    [11] K. F. Sonnen et al. 3D-structured illumination microscopy provides novel insight into architecture of human centrosomes. Biol. Open, 1, 965-976(2012).

    [12] A. R. Nair et al. The microcephaly-associated protein Wdr62/CG7337 is required to maintain centrosome asymmetry in Drosophila neuroblasts. Cell Rep., 14, 1100-1113(2016).

    [13] M. A. Ricci et al. Chromatin fibers are formed by heterogeneous groups of nucleosomes in vivo. Cell, 160, 1145-1158(2015).

    [14] A. N. Boettiger et al. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature, 529, 418-422(2016).

    [15] C. A. Wurm et al. Nanoscale distribution of mitochondrial import receptor Tom20 is adjusted to cellular conditions and exhibits an inner-cellular gradient. Proc. Natl. Acad. Sci. U. S. A., 108, 13546-13551(2011).

    [16] H. D. Wang et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 16, 103-110(2019).

    [17] R. Chen et al. Deep-learning super-resolution microscopy reveals nanometer-scale intracellular dynamics at the millisecond temporal resolution(2021).

    [18] C. Qiao et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods, 18, 194-202(2021).

    [19] P. Llull et al. Coded aperture compressive temporal imaging. Opt. Express, 21, 10526-10545(2013).

    [20] M. Qiao, X. Liu, X. Yuan. Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks. Opt. Lett., 46, 1888-1891(2021).

    [21] A. Paliwal, N. K. Kalantari. Deep slow motion video reconstruction with hybrid imaging system. IEEE Trans. Pattern Anal. Mach. Intell., 42, 1557-1569(2020).

    [22] H. Z. Jiang et al. Super SloMo: high quality estimation of multiple intermediate frames for video interpolation. Proc. IEEE/CVF Conf. Comput. Vision and Pattern Recognit., 9000-9008(2018).

    [23] T. Goldstein, S. Osher. The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci., 2, 323-343(2009).

    [24] P. Tseng, S. Yun. A coordinate gradient descent method for nonsmooth separable minimization. Math. Prog., 117, 387-423(2009).

    [25] K. Zhang, W. M. Zuo, L. Zhang. FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process., 27, 4608-4622(2018).

    [26] M. Tassano et al. FastDVDnet: towards real-time deep video denoising without flow estimation. Proc. IEEE/CVF Conf. Comput. Vision and Pattern Recognit., 1354-1363(2020).

    [27] J. H. Lu, M. L. Liou. A simple and efficient search algorithm for block-matching motion estimation. IEEE Trans. Circuits Syst. Video Technol., 7, 429-433(1997).

    [28] J.-Y. Bouguet. Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm. Intel Corp., 5, 1-10(2001).

    [29] G. R. Wang, F. Yang, W. Zhao. There can be turbulence in microfluidics at low Reynolds number. Lab Chip, 14, 1452-1458(2014).

    [30] E. E. Michaelides. Brownian movement and thermophoresis of nanoparticles in liquids. Int. J. Heat Mass Transf., 81, 179-187(2015).

    [31] F. Z. Zhuang et al. A comprehensive survey on transfer learning. Proc. IEEE, 109, 43-76(2021).

    [32] A. Creswell et al. Generative adversarial networks an overview. IEEE Signal Process. Mag., 35, 53-65(2018).

    [33] D. Ulyanov et al. Deep image prior, 9446-9454(2018).

    [34] R. J. Yang, L. M. Fu, H. H. Hou. Review and perspectives on microfluidic flow cytometers. Sens. Actuator B Chem., 266, 26-45(2018).

    [35] M. Schrader et al. The different facets of organelle interplay-an overview of organelle interactions. Front. Cell. Dev. Biol., 3, 56(2015).

    [36] R. D. Vale. The molecular motor toolbox for intracellular transport. Cell, 112, 467-480(2003).

    [37] Y. Y. Gong et al. High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor. Science, 350, 1361-1366(2015).

    [38] P. Schelkens et al. Compression strategies for digital holograms in biomedical and multimedia applications. Light Adv. Manuf., 3, 40(2022).

    [39] Z. Y. Chen et al. Physics-driven deep learning enables temporal compressive coherent diffraction imaging. Optica, 9, 677-680(2022).

    [40] Y. Hu et al. Microscopic fringe projection profilometry: a review. Opt. Lasers Eng., 135, 106192(2020).

    Yilin He, Yunhua Yao, Dalong Qi, Yu He, Zhengqi Huang, Pengpeng Ding, Chengzhi Jin, Chonglei Zhang, Lianzhong Deng, Kebin Shi, Zhenrong Sun, Xiaocong Yuan, Shian Zhang. Temporal compressive super-resolution microscopy at frame rate of 1200 frames per second and spatial resolution of 100 nm[J]. Advanced Photonics, 2023, 5(2): 026003
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