Researching | Fast super-resolution optical fluctuation imaging using a transformer-optimized neural network

Advanced Imaging, Volume. 2, Issue 4, 041004(2025)

Fast super-resolution optical fluctuation imaging using a transformer-optimized neural network

Zitong Ye¹, Yuran Huang¹, Hanchu Ye¹, Enxing He¹, Yile Sun¹, Haoyu Zhou¹, Xin Luo¹, Yubing Han^1,3、*, Cuifang Kuang^1,2,4、*, and Xu Liu^1,2

Author Affiliations

¹State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China

²ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China

³Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

⁴Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China

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References(41)

[1] B. Andrews et al. Imaging cell biology. Nat. Cell Biol., 24, 1180(2022). https://doi.org/10.1038/s41556-022-00960-6

[2] L. Schermelleh et al. Super-resolution microscopy demystified. Nat. Cell Biol., 21, 72(2019). https://doi.org/10.1038/s41556-018-0251-8

[3] Y. Huang et al. Multiplexed stimulated emission depletion nanoscopy (mSTED) for 5-color live-cell long-term imaging of organelle interactome. Opto-Electron. Adv., 7, 240035(2024). https://doi.org/10.29026/oea.2024.240035

[4] Y. Sun et al. Fluorescence interference structured illumination microscopy for 3D morphology imaging with high axial resolution. Adv. Photonics, 5, 056007(2023). https://doi.org/10.1117/1.AP.5.5.056007

[5] E. Betzig et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science, 313, 1642(2006). https://doi.org/10.1126/science.1127344

[6] N. Bourg et al. Direct optical nanoscopy with axially localized detection. Nat. Photonics, 9, 587(2015). https://doi.org/10.1038/nphoton.2015.132

[7] M. Lelek et al. Single-molecule localization microscopy. Nat. Rev. Methods Primers, 1, 39(2021). https://doi.org/10.1038/s43586-021-00038-x

[8] S. Geissbuehler, C. Dellagiacoma, T. Lasser. Comparison between SOFI and STORM. Biomed. Opt. Express, 2, 408(2011). https://doi.org/10.1364/BOE.2.000408

[9] R. Jungmann et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods, 11, 313(2014). https://doi.org/10.1038/nmeth.2835

[10] G. T. Dempsey et al. Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging. Nat. Methods, 8, 1027(2011). https://doi.org/10.1038/nmeth.1768

[11] S. Basak et al. Super-resolution optical fluctuation imaging. Nat. Photonics, 19, 229(2025). https://doi.org/10.1038/s41566-024-01571-3

[12] T. Dertinger et al. Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI). Proc. Natl. Acad. Sci. USA, 106, 22287(2009). https://doi.org/10.1073/pnas.0907866106

[13] S. Geissbuehler et al. Live-cell multiplane three-dimensional super-resolution optical fluctuation imaging. Nat. Commun., 5, 5830(2014). https://doi.org/10.1038/ncomms6830

[14] A. Sroda et al. SOFISM: Super-resolution optical fluctuation image scanning microscopy. Optica, 7, 1308(2020). https://doi.org/10.1364/OPTICA.399600

[15] M. Pawlowska et al. Embracing the uncertainty: the evolution of SOFI into a diverse family of fluctuation-based super-resolution microscopy methods. J. Phys., 4, 012002(2022). https://doi.org/10.1088/2515-7647/ac3838

[16] K. Grußmayer et al. Self-blinking dyes unlock high-order and multiplane super-resolution optical fluctuation imaging. ACS Nano, 14, 9156(2020). https://doi.org/10.1021/acsnano.0c04602

[17] W. Vandenberg et al. Model-free uncertainty estimation in stochastical optical fluctuation imaging (SOFI) leads to a doubled temporal resolution. Biomed. Opt. Express, 7, 467(2016). https://doi.org/10.1364/BOE.7.000467

[18] S. Jiang et al. Enhanced SOFI algorithm achieved with modified optical fluctuating signal extraction. Opt. Express, 24, 3037(2016). https://doi.org/10.1364/OE.24.003037

[19] W. Zhao et al. Enhanced detection of fluorescence fluctuations for high-throughput super-resolution imaging. Nat. Photonics, 17, 806(2023). https://doi.org/10.1038/s41566-023-01234-9

[20] A. Yemets et al. Quantum dot-antibody conjugates for immunofluorescence studies of biomolecules and subcellular structures. J. Fluorescence, 32, 1713(2022). https://doi.org/10.1007/s10895-022-02968-5

[21] M. B. Elowitz et al. Stochastic gene expression in a single cell. Science, 297, 1183(2002). https://doi.org/10.1126/science.1070919

[22] X. Liu et al. Reconstruction of structured illumination microscopy with an untrained neural network. Opt. Commun., 537, 129431(2023). https://doi.org/10.1016/j.optcom.2023.129431

[23] H. Wu et al. Single-frame structured illumination microscopy for fast live-cell imaging. APL Photonics, 9, 036102(2024). https://doi.org/10.1063/5.0180978

[24] Z. Ye et al. Untrained neural network enabling fast and universal structured-illumination microscopy. Opt. Lett., 49, 2205(2024). https://doi.org/10.1364/OL.511983

[25] W. Ouyang et al. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol., 36, 460(2018). https://doi.org/10.1038/nbt.4106

[26] E. Nehme et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, 5, 458(2018). https://doi.org/10.1364/OPTICA.5.000458

[27] K. K. Narayanasamy et al. Fast DNA-PAINT imaging using a deep neural network. Nat. Commun., 13, 5047(2022). https://doi.org/10.1038/s41467-022-32626-0

[28] L. M. Beck et al. Improving correlation based super-resolution microscopy images through image fusion by self-supervised deep learning. Opt. Express, 32, 28195(2024). https://doi.org/10.1364/OE.521577

[29] Y. Deng et al. T-former: an efficient transformer for image inpainting, 6559(2022).

[30] Z. Liu et al. Swin transformer: hierarchical vision transformer using shifted windows, 9992(2021).

[31] S. Geissbuehler et al. Mapping molecular statistics with balanced super-resolution optical fluctuation imaging (bSOFI). Opt. Nanoscopy, 1, 4(2012). https://doi.org/10.1186/2192-2853-1-4

[32] A. Descloux, K. S. Grußmayer, A. Radenovic. Parameter-free image resolution estimation based on decorrelation analysis. Nat. Methods, 16, 918(2019). https://doi.org/10.1038/s41592-019-0515-7

[33] Y. Guo et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell, 175, 1430(2018). https://doi.org/10.1016/j.cell.2018.09.057

[34] A. Girsault et al. SOFI simulation tool: a software package for simulating and testing super-resolution optical fluctuation imaging. PLOS One, 11, e0161602(2016). https://doi.org/10.1371/journal.pone.0161602

[35] G. Wen et al. High-fidelity structured illumination microscopy by point-spread-function engineering. Light: Sci. Appl., 10, 70(2021). https://doi.org/10.1038/s41377-021-00513-w

[36] R. M. Power et al. Build and operation of a custom 3D, multicolor, single-molecule localization microscope. Nat. Protocols, 19, 2467(2024). https://doi.org/10.1038/s41596-024-00989-x

[37] W. Xue et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process., 23, 684(2014). https://doi.org/10.1109/TIP.2013.2293423

[38] Y. He et al. Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels. Adv. Photonics Nexus, 2, 046005(2023). https://doi.org/10.1117/1.APN.2.4.046005

[39] A. Bansal et al. Cold diffusion: inverting arbitrary image transforms without noise(2023).

[40] W. Zhang et al. Self-supervised PSF-informed deep learning enables real-time deconvolution for optical coherence tomography. Adv. Imaging, 2, 021001(2025). https://doi.org/10.3788/AI.2025.10026

[41] https:/github.com/ZJUOPTKuangLab/TRUS.

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Zitong Ye, Yuran Huang, Hanchu Ye, Enxing He, Yile Sun, Haoyu Zhou, Xin Luo, Yubing Han, Cuifang Kuang, Xu Liu, "Fast super-resolution optical fluctuation imaging using a transformer-optimized neural network," Adv. Imaging 2, 041004 (2025)

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Paper Information

Category: Research Article

Received: May. 15, 2025

Accepted: Jul. 3, 2025

Published Online: Jul. 31, 2025

The Author Email: Yubing Han (hanyubing@zju.edu.cn), Cuifang Kuang (cfkang@zju.edu.cn)

DOI:10.3788/AI.2025.10011

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