Superresolution structured illumination microscopy reconstruction algorithms: a review

Xin Chen; Suyi Zhong; Yiwei Hou; Ruijie Cao; Wenyi Wang; Dong Li; Qionghai Dai; Donghyun Kim; Peng Xi

doi:10.1038/s41377-023-01204-4

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Superresolution structured illumination microscopy reconstruction algorithms: a review

Reviews

- Superresolution structured illumination microscopy reconstruction algorithms: a review
- Superresolution structured illumination microscopy reconstruction algorithms: a review
- LSA 2023年12卷第8期页码：1510-1543
- Affiliations：
  
  1.Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China
  2.National Biomedical Imaging Center, Peking University, Beijing 100871, China
  3.National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
  4.Department of Automation, Tsinghua University, Beijing, China
  5.Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
  6.Beijing Key Laboratory of Multidimension & Multiscale Computational Photography, Tsinghua University, Beijing, China
  7.Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
  8.School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul 03722, Korea
- Author bio：
  
  Peng Xi (xipeng@pku.edu.cn)
- Funds：
- DOI：10.1038/s41377-023-01204-4
  中图分类号：
- 纸质出版日期：2023-08-31，
  
  网络出版日期：2023-07-12，
  
  收稿日期：2022-12-05，
  
  修回日期：2023-05-24，
  
  录用日期：2023-06-05
- Accepted：
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Superresolution structured illumination microscopy reconstruction algorithms: a review[J]. LSA, 2023,12(8):1510-1543.

Chen, X. et al. Superresolution structured illumination microscopy reconstruction algorithms: a review. Light: Science & Applications, 12, 1510-1543 (2023).
Superresolution structured illumination microscopy reconstruction algorithms: a review[J]. LSA, 2023,12(8):1510-1543. DOI： 10.1038/s41377-023-01204-4.

Chen, X. et al. Superresolution structured illumination microscopy reconstruction algorithms: a review. Light: Science & Applications, 12, 1510-1543 (2023). DOI： 10.1038/s41377-023-01204-4.

摘要

Abstract

Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy

offering ultrahigh imaging speed

superresolution

a large field-of-view

and long-term imaging. Over the past decade

SIM hardware and software have flourished

leading to successful applications in various biological questions. However

unlocking the full potential of SIM system hardware requires the development of advanced reconstruction algorithms. Here

we introduce the basic theory of two SIM algorithms

namely

optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM)

and summarize their implementation modalities. We then provide a brief overview of existing OS-SIM processing algorithms and review the development of SR-SIM reconstruction algorithms

focusing primarily on 2D-SIM

3D-SIM

and blind-SIM. To showcase the state-of-the-art development of SIM systems and assist users in selecting a commercial SIM system for a specific application

we compare the features of representative off-the-shelf SIM systems. Finally

we provide perspectives on the potential future developments of SIM.

关键词

Keywords

references

Cole, F. J. Alle de brieven van Antoni van Leeuwenhoek.Nature144, 956–958, https://doi.org/10.1038/144956a0 (1939)..

Abbe, E. Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung.Arch. f. ür. Mikroskopische Anat.9, 413–468, https://doi.org/10.1007/BF02956173 (1873)..

Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution.Science313, 1642–1645, https://doi.org/10.1126/science.1127344 (2006)..

Betzig, E. et al. Breaking the diffraction barrier: optical microscopy on a nanometric scale.Science251, 1468–1470, https://doi.org/10.1126/science.251.5000.1468 (1991)..

Hess, S. T., Girirajan, T. P. K.&Mason, M. D. Ultra-high resolution imaging by fluorescence photoactivation localization microscopy.Biophys. J.91, 4258–4272, https://doi.org/10.1529/biophysj.106.091116 (2006)..

Rust, M. J., Bates, M.&Zhuang, X. W. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM).Nat. Methods3, 793–795, https://doi.org/10.1038/nmeth929 (2006)..

Sharonov, A.&Hochstrasser, R. M. Wide-field subdiffraction imaging by accumulated binding of diffusing probes.Proc. Natl Acad. Sci. USA103, 18911–18916, https://doi.org/10.1073/pnas.0609643104 (2006)..

Hell, S. W.&Wichmann, J. Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy.Opt. Lett.19, 780–782, https://doi.org/10.1364/ol.19.000780 (1994)..

Klar, T. A.&Hell, S. W. Subdiffraction resolution in far-field fluorescence microscopy.Opt. Lett.24, 954–956, https://doi.org/10.1364/ol.24.000954 (1999)..

Klar, T. A. et al. Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission.Proc. Natl Acad. Sci. USA97, 8206–8210, https://doi.org/10.1073/pnas.97.15.8206 (2000)..

Gustafsson, M. G. L. Surpassing the lateralresolution limit by a factor of two using structured illumination microscopy: short communication.J. Microsc.198, 82–87, https://doi.org/10.1046/j.1365-2818.2000.00710.x (2000)..

Gustafsson, M. G. L. et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination.Biophys. J.94, 4957–4970, https://doi.org/10.1529/biophysj.107.120345 (2008)..

Heintzmann, R.&Cremer, C. G. Laterally modulated excitation microscopy: improvement of resolution by using a diffraction grating. InProceedings of SPIE 3568, Optical Biopsies and Microscopic Techniques III185–196,https://doi.org/10.1117/12.336833https://doi.org/10.1117/12.336833. (SPIE, 1999).

Gustafsson, M. G. L. Extended resolution fluorescence microscopy.Curr. Opin. Struct. Biol.9, 627–628, https://doi.org/10.1016/s0959-440x(99)00016-0 (1999)..

Heintzmann, R.&Huser, T. Super-resolution structured illumination microscopy.Chem. Rev.117, 13890–13908, https://doi.org/10.1021/acs.chemrev.7b00218 (2017)..

Bailey, B. et al. Three-dimensional imaging of biological specimens with standing wave fluorescence microscopy. InProceedings of SPIE 2184, Three-Dimensional Microscopy: Image Acquisition and Processing208–213 (SPIE, 1994).

Neil, M. A. A., Jušskaitis, R.&Wilson, T. Method of obtaining optical sectioning by using structured light in a conventional microscope.Opt. Lett.22, 1905–1907, https://doi.org/10.1364/ol.22.001905 (1997)..

Karadaglić, D. Image formation in conventional brightfield reflection microscopes with optical sectioning property via structured illumination.Micron39, 302–310, https://doi.org/10.1016/j.micron.2007.10.001 (2008)..

Karadaglić, D.&Wilson, T. Image formation in structured illumination wide-field fluorescence microscopy.Micron39, 808–818, https://doi.org/10.1016/j.micron.2008.01.017 (2008)..

Gustafsson, M. G. L. The author file: Mats Gustafsson (1960-2011).Nat. Methods8, 439, https://doi.org/10.1038/nmeth.1612 (2011)..

Gustafsson, M. G. L., Agard, D. A.&Sedat, J. W. Sevenfold improvement of axial resolution in 3D wide-field microscopy using two objective lenses. InProceedings of SPIE 2412, Three-Dimensional Microscopy: Image Acquisition and Processing II147–156 (SPIE, 1995).

Streibl, N. Three-dimensional imaging by a microscope.J. Opt. Soc. Am.2, 121–127, https://doi.org/10.1364/JOSAA.2.000121 (1985)..

Neil, M. A. A., Juškaitis, R.&Wilson, T. Real time 3D fluorescence microscopy by two beam interference illumination.Opt. Commun.153, 1–4, https://doi.org/10.1016/S0030-4018(98)00210-7 (1998)..

Fahrbach, F. O. et al. Rapid 3D light-sheet microscopy with a tunable lens.Opt. Express21, 21010–21026, https://doi.org/10.1364/oe.21.021010 (2013)..

Power, R. M.&Huisken, J. Adaptable, illumination patterning light sheet microscopy.Sci. Rep.8, 9615, https://doi.org/10.1038/s41598-018-28036-2 (2018)..

Reynaud, E. G. et al. Guide to light-sheet microscopy for adventurous biologists.Nat. Methods12, 30–34, https://doi.org/10.1038/nmeth.3222 (2015)..

Lal, A., Shan, C. Y.&Xi, P. Structured illumination microscopy image reconstruction algorithm.IEEE J. Sel. Top. Quantum Electron.22, 6803414, https://doi.org/10.1109/JSTQE.2016.2521542 (2016)..

Shroff, S. A., Fienup, J. R.&Williams, D. R. OTF compensation in structured illumination superresolution images. InProceedings of SPIE 7094, Unconventional Imaging IV709402.https://doi.org/10.1117/12.791052https://doi.org/10.1117/12.791052. (SOIE, 2008).

Yaroslavsky, L. P.&Caulfield, H. J. Deconvolution of multiple images of the same object.Appl. Opt.33, 2157–2162, https://doi.org/10.1364/ao.33.002157 (1994)..

Heintzmann, R.&Benedetti, P. A. High-resolution image reconstruction in fluorescence microscopy with patterned excitation.Appl. Opt.45, 5037–5045, https://doi.org/10.1364/AO.45.005037 (2006)..

Schropp, M.&Uhl, R. Two-dimensional structured illumination microscopy.J. Microsc.256, 23–36, https://doi.org/10.1111/jmi.12154 (2014)..

Zhang, C. L., Xu, N.&Tan, Q. F. Compact structured illumination microscopy with high spatial frequency diffractive lattice patterns.Biomed. Opt. Express13, 6113–6123, https://doi.org/10.1364/boe.473899 (2022)..

Ventalon, C., Heintzmann, R.&Mertz, J. Dynamic speckle illumination microscopy with wavelet prefiltering.Opt. Lett.32, 1417–1419, https://doi.org/10.1364/ol.32.001417 (2007)..

Ventalon, C.&Mertz, J. Quasi-confocal fluorescence sectioning with dynamic speckle illumination.Opt. Lett.30, 3350–3352, https://doi.org/10.1364/ol.30.003350 (2005)..

Mangeat, T. et al. Super-resolved live-cell imaging using random illumination microscopy.Cell Rep. Methods1, 100009, https://doi.org/10.1016/j.crmeth.2021.100009 (2021)..

Lim, D., Chu, K. K.&Mertz, J. Wide-field fluorescence sectioning with hybrid speckle and uniform-illumination microscopy.Opt. Lett.33, 1819–1821, https://doi.org/10.1364/ol.33.001819 (2008)..

Philipp, K. et al. Volumetric HiLo microscopy employing an electrically tunable lens.Opt. Express24, 15029–15041, https://doi.org/10.1364/oe.24.015029 (2016)..

Appelt, D. et al. Polarized illumination coded structured illumination microscopy (picoSIM): experimental results.Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.380, 20210193, https://doi.org/10.1098/rsta.2021.0193 (2022)..

Wicker, K.&Heintzmann, R. Single-shot optical sectioning using polarization-coded structured illumination.J. Opt.12, 084010, https://doi.org/10.1088/2040-8978/12/8/084010 (2010)..

Mandula, O. et al. Line scan-structured illumination microscopy super-resolution imaging in thick fluorescent samples.Opt. Express20, 24167–24174, https://doi.org/10.1364/oe.20.024167 (2012)..

Zhang, H. J., Vyas, K.&Yang, G. Z. Line scanning, fiber bundle fluorescence HiLo endomicroscopy with confocal slit detection.J. Biomed. Opt.24, 116501, https://doi.org/10.1117/1.Jbo.24.11.116501 (2019)..

Qiao, W. et al. Single-scan HiLo with line-illumination strategy for optical section imaging of thick tissues.Biomed. Opt. Express12, 2373–2383, https://doi.org/10.1364/boe.419377 (2021)..

Zhong, Q. Y. et al. High-definition imaging using line-illumination modulation microscopy.Nat. Methods18, 309–315, https://doi.org/10.1038/s41592-021-01074-x (2021)..

Fu, Z. Q. et al. Single-shot optical sectioning microscopy based on structured illumination.Opt. Lett.47, 814–817, https://doi.org/10.1364/ol.451267 (2022)..

Gustafsson, M. G. L., Agard, D. A.&Sedat, J. W. I5M: 3D widefield light microscopy with better than 100 nm axial resolution.J. Microsc.195, 10–16, https://doi.org/10.1046/j.1365-2818.1999.00576.x (1999)..

Agard, D. A. et al. Fluorescence microscopy in three dimensions.Methods Cell Biol.30, 353–374, https://doi.org/10.1016/s0091-679x(08)60986-3 (1989). 374a, 375-377..

Manton, J. D. et al. Concepts for structured illumination microscopy with extended axial resolution through mirrored illumination.Biomed. Opt. Express11, 2098–2108, https://doi.org/10.1364/boe.382398 (2020)..

Li, X. S. et al. Three-dimensional structured illumination microscopy with enhanced axial resolution.Nat. Biotechnol.https://doi.org/10.1038/s41587-022-01651-1 (2023).

Shao, L. et al. I5S: wide-field light microscopy with 100-nm-scale resolution in three dimensions.Biophys. J.94, 4971–4983, https://doi.org/10.1529/biophysj.107.120352 (2008)..

Liu, Q. L. et al. Sub-60-nm 3D super-resolution imaging via saturated I5S.Opt. Commun.473, 125981, https://doi.org/10.1016/j.optcom.2020.125981 (2020)..

Melnikov, A.&Lun, D. P. K. 3D imaging with moving fringe structured illumination microscopy.IEEE Photonics J.14, 3923710, https://doi.org/10.1109/jphot.2022.3166630 (2022)..

Hirvonen, L. M. et al. Structured illumination microscopy of a living cell.Eur. Biophys. J.38, 807–812, https://doi.org/10.1007/s00249-009-0501-6 (2009)..

Kner, P. et al. Super-resolution video microscopy of live cells by structured illumination.Nat. Methods6, 339–342, https://doi.org/10.1038/nmeth.1324 (2009)..

Shao, L. et al. Super-resolution 3D microscopy of live whole cells using structured illumination.Nat. Methods8, 1044–1046, https://doi.org/10.1038/nmeth.1734 (2011)..

York, A. G. et al. Resolution doubling in live, multicellular organisms via multifocal structured illumination microscopy.Nat. Methods9, 749–754, https://doi.org/10.1038/nmeth.2025 (2012)..

Li, D. et al. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics.Science349, aab3500, https://doi.org/10.1126/science.aab3500 (2015)..

Nixon-Abell, J. et al. Increased spatiotemporal resolution reveals highly dynamic dense tubular matrices in the peripheral ER.Science354, aaf3928, https://doi.org/10.1126/science.aaf3928 (2016)..

Curd, A. et al. Construction of an instant structured illumination microscope.Methods88, 37–47, https://doi.org/10.1016/j.ymeth.2015.07.012 (2015)..

York, A. G. et al. Instant super-resolution imaging in live cells and embryos via analog image processing.Nat. Methods10, 1122–1126, https://doi.org/10.1038/nmeth.2687 (2013)..

Abrahamsson, S. et al. Multifocus structured illumination microscopy for fast volumetric super-resolution imaging.Biomed. Opt. Express8, 4135–4140, https://doi.org/10.1364/boe.8.004135 (2017)..

Chen, Y. H. et al. Multi-color live-cell super-resolution volume imaging with multi-angle interference microscopy.Nat. Commun.9, 4818, https://doi.org/10.1038/s41467-018-07244-4 (2018)..

Liu, W. J. et al. Three-dimensional super-resolution imaging of live whole cells using galvanometer-based structured illumination microscopy.Opt. Express27, 7237–7248, https://doi.org/10.1364/oe.27.007237 (2019)..

Xu, F. H. et al. Real-time reconstruction using electro-optics modulator-based structured illumination microscopy.Opt. Express30, 13238–13251, https://doi.org/10.1364/oe.454982 (2022)..

Gustafsson, M. G. L. Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution.Proc. Natl Acad. Sci. USA102, 13081–13086, https://doi.org/10.1073/pnas.0406877102 (2005)..

Heintzmann, R., Jovin, T. M.&Cremer, C. Saturated patterned excitation microscopy—a concept for optical resolution improvement.J. Opt. Soc. Am.19, 1599–1609, https://doi.org/10.1364/JOSAA.19.001599 (2002)..

Ingerman, E. A. et al. Signal, noise and resolution in linear and nonlinear structured-illumination microscopy.J. Microsc.273, 3–25, https://doi.org/10.1111/jmi.12753 (2019)..

Heintzmann, R. Saturated patterned excitation microscopy with two-dimensional excitation patterns.Micron34, 283–291, https://doi.org/10.1016/S0968-4328(03)00053-2 (2003)..

Rego, E. H. et al. Nonlinear structured-illumination microscopy with a photoswitchable protein reveals cellular structures at 50-nm resolution.Proc. Natl Acad. Sci. USA109, E135–E143, https://doi.org/10.1073/pnas.1107547108 (2011)..

Andresen, M. et al. Photoswitchable fluorescent proteins enable monochromatic multilabel imaging and dual color fluorescence nanoscopy.Nat. Biotechnol.26, 1035–1040, https://doi.org/10.1038/nbt.1493 (2008)..

Wei, F. F.&Liu, Z. W. Plasmonic structured illumination microscopy.Nano Lett.10, 2531–2536, https://doi.org/10.1021/nl1011068 (2010)..

Wei, F. F. et al. Wide field super-resolution surface imaging through plasmonic structured illumination microscopy.Nano Lett.14, 4634–4639, https://doi.org/10.1021/nl501695c (2014)..

Samanta, K. et al. Saturable absorption assisted nonlinear structured illumination microscopy.Opt. Lett.47, 2702–2705, https://doi.org/10.1364/ol.460502 (2022)..

Zhang, H., Zhao, M.&Peng, L. L. Nonlinear structured illumination microscopy by surface plasmon enhanced stimulated emission depletion.Opt. Express19, 24783–24794, https://doi.org/10.1364/oe.19.024783 (2011)..

Dake, F., Nakayama, S.&Taki, Y. Optical resolution enhancement and background reduction by stimulated emission depletion structured illumination microscopy with structured excitation. InProceedings of Novel Techniques in Microscopy 2015(Optica Publishing Group, 2015)https://doi.org/10.1364/NTM.2015.NM2C.4https://doi.org/10.1364/NTM.2015.NM2C.4..

Xue, Y.&So, P. T. C. Three-dimensional super-resolution high-throughput imaging by structured illumination STED microscopy.Opt. Express26, 20920–20928, https://doi.org/10.1364/oe.26.020920 (2018)..

Meng, Y. L. et al. Fast two-snapshot structured illumination for temporal focusing microscopy with enhanced axial resolution.Opt. Express25, 23109–23121, https://doi.org/10.1364/OE.25.023109 (2017)..

Patorski, K., Trusiak, M.&Tkaczyk, T. Optically-sectioned two-shot structured illumination microscopy with Hilbert-Huang processing.Opt. Express22, 9517–9527, https://doi.org/10.1364/oe.22.009517 (2014)..

Wang, H. T. et al. One-shot optical sectioning structured illumination microscopy. InProc. SPIE113380F (SPIE, 2019).

Biggs, D. S. C. 3D deconvolution microscopy.Curr. Protoc. Cytom.https://doi.org/10.1002/0471142956.cy1219s52 (2010).

Sarder, P.&Nehorai, A. Deconvolution methods for 3-D fluorescence microscopy images.IEEE Signal Process. Mag.23, 32–45, https://doi.org/10.1109/MSP.2006.1628876 (2006)..

Klug, A.&Crowther, R. A. Three-dimensional image reconstruction from the viewpoint of information theory.Nature238, 435–440, https://doi.org/10.1038/238435a0 (1972)..

Lucy, L. B. An iterative technique for the rectification of observed distributions.Astron. J.79, 745, https://doi.org/10.1086/111605 (1974)..

Richardson, W. H. Bayesian-based iterative method of image restoration.J. Opt. Soc. Am.62, 55–59 (1972)..

Zhang, X. Y. et al. Deep learning optical-sectioning method.Opt. Express26, 30762–30772, https://doi.org/10.1364/OE.26.030762 (2018)..

Chai, C. C. et al. Deep learning based one-shot optically-sectioned structured illumination microscopy for surface measurement.Opt. Express29, 4010–4021, https://doi.org/10.1364/oe.415210 (2021)..

Schaefer, L. H., Schuster, D.&Schaffer, J. Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach.J. Microsc.216, 165–174, https://doi.org/10.1111/j.0022-2720.2004.01411.x (2004)..

Demmerle, J. et al. Strategic and practical guidelines for successful structured illumination microscopy.Nat. Protoc.12, 988–1010, https://doi.org/10.1038/nprot.2017.019 (2017)..

Li, M. Q. et al. Structured illumination microscopy using digital micro-mirror device and coherent light source.Appl. Phys. Lett.116, 233702, https://doi.org/10.1063/5.0008264 (2020)..

Müller, M. et al. Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ.Nat. Commun.7, 10980, https://doi.org/10.1038/ncomms10980 (2016)..

Schneider, C. A., Rasband, W. S.&Eliceiri, K. W. NIH image to ImageJ: 25 years of image analysis.Nat. Methods9, 671–675, https://doi.org/10.1038/nmeth.2089 (2012)..

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

Shroff, S. A., Fienup, J. R.&Williams, D. R. Phase-shift estimation in sinusoidally illuminated images for lateral superresolution.J. Opt. Soc. Am.26, 413–424, https://doi.org/10.1364/josaa.26.000413 (2009)..

Shroff, S. A., Fienup, J. R.&Williams, D. R. Lateral superresolution usinga posterioriphase shift estimation for a moving object: experimental results.J. Opt. Soc. Am. A27, 1770–1782, https://doi.org/10.1364/JOSAA.27.001770 (2010)..

Wicker, K. et al. Phase optimisation for structured illumination microscopy.Opt. Express21, 2032–2049, https://doi.org/10.1364/oe.21.002032 (2013)..

Wicker, K. Non-iterative determination of pattern phase in structured illumination microscopy using auto-correlations in Fourier space.Opt. Express21, 24692–24701, https://doi.org/10.1364/oe.21.024692 (2013)..

Zhou, X. et al. Image recombination transform algorithm for superresolution structured illumination microscopy.J. Biomed. Opt.21, 096009, https://doi.org/10.1117/1.Jbo.21.9.096009 (2016)..

Zhao, T. Y. et al. Multi-color structured illumination microscopy for live cell imaging based on the enhanced image recombination transform algorithm.Biomed. Opt. Express12, 3474–3484, https://doi.org/10.1364/boe.423171 (2021)..

Cao, R. Z. et al. Inverse matrix based phase estimation algorithm for structured illumination microscopy.Biomed. Opt. Express9, 5037–5051, https://doi.org/10.1364/boe.9.005037 (2018)..

Qian, J. M. et al. Structured illumination microscopy based on principal component analysis.eLight3, 4, https://doi.org/10.1186/s43593-022-00035-x (2023)..

Chen, X., Hou, Y. W.&Xi, P. Parameter estimation of the structured illumination pattern based on principal component analysis (PCA): PCA-SIM.Light Sci. Appl.12, 41, https://doi.org/10.1038/s41377-022-01043-9 (2023)..

Sahl, S. J. et al. Comment on "Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics".Science352, 527, https://doi.org/10.1126/science.aad7983 (2016)..

Booth, M. et al. Aberrations and adaptive optics in super-resolution microscopy.Microscopy64, 251–261, https://doi.org/10.1093/jmicro/dfv033 (2015)..

Thomas, B. et al. Enhanced resolution through thick tissue with structured illumination and adaptive optics.J. Biomed. Opt.20, 026006, https://doi.org/10.1117/1.Jbo.20.2.026006 (2015)..

Li, Z. W. et al. Fast widefield imaging of neuronal structure and function with optical sectioning in vivo.Sci. Adv.6, eaaz3870, https://doi.org/10.1126/sciadv.aaz3870 (2020)..

Lin, R. Z. et al. Subcellular three-dimensional imaging deep through multicellular thick samples by structured illumination microscopy and adaptive optics.Nat. Commun.12, 3148, https://doi.org/10.1038/s41467-021-23449-6 (2021)..

Turcotte, R. et al. Dynamic super-resolution structured illumination imaging in the living brain.Proc. Natl Acad. Sci. USA116, 9586–9591, https://doi.org/10.1073/pnas.1819965116 (2019)..

Bilsing, C. et al. 3D imaging with double-helix point spread function and dynamic aberration correction using a deformable mirror.Opt. Lasers Eng.154, 107044, https://doi.org/10.1016/j.optlaseng.2022.107044 (2022)..

Perez, V., Chang, B. J.&Stelzer, E. H. K. Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution.Sci. Rep.6, 37149, https://doi.org/10.1038/srep37149 (2016)..

Hoffman, D. P.&Betzig, E. Tiled reconstruction improves structured illumination microscopy. Preprint atbioRxivhttps://doi.org/10.1101/2020.01.06.895318https://doi.org/10.1101/2020.01.06.895318(2020).

Johnson, K. A.&Hagen, G. M. Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction.GigaScience9, giaa035, https://doi.org/10.1093/gigascience/giaa035 (2020)..

Ingaramo, M. et al. Richardson-lucy deconvolution as a general tool for combining images with complementary strengths.Chemphyschem15, 794–800, https://doi.org/10.1002/cphc.201300831 (2014)..

Chu, K. Q. et al. Image reconstruction for structured-illumination microscopy with low signal level.Opt. Express22, 8687–8702, https://doi.org/10.1364/oe.22.008687 (2014)..

Lukeš, T. et al. Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation.Opt. Express22, 29805–29817, https://doi.org/10.1364/OE.22.029805 (2014)..

Křížek, P. et al. SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.Bioinformatics32, 318–320, https://doi.org/10.1093/bioinformatics/btv576 (2016)..

Huang, X. S. et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy.Nat. Biotechnol.36, 451–459, https://doi.org/10.1038/nbt.4115 (2018)..

Boulanger, J. et al. Nonsmooth convex optimization for structured illumination microscopy image reconstruction.Inverse Probl.34, 095004, https://doi.org/10.1088/1361-6420/aaccca (2018)..

Yu, W. et al. Second-order optimized regularized structured illumination microscopy (sorSIM) for high-quality and rapid super resolution image reconstruction with low signal level.Opt. Express28, 16708–16724, https://doi.org/10.1364/oe.390745 (2020)..

Zhao, W. S. et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy.Nat. Biotechnol.40, 606–617, https://doi.org/10.1038/s41587-021-01092-2 (2022)..

Zhou, B. et al. sCMOS noise-corrected superresolution reconstruction algorithm for structured illumination microscopy.Photonics9, 172, https://doi.org/10.3390/photonics9030172 (2022)..

Van der Vorst, H. A. Bi-CGSTAB: a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems.SIAM J. Sci. Stat. Comput.13, 631–644, https://doi.org/10.1137/0913035 (1992)..

Abascal, J. F. P. J. et al. Fluorescence diffuse optical tomography using the split Bregman method.Med. Phys.38, 6275–6284, https://doi.org/10.1118/1.3656063 (2011)..

Hou, Y. W. et al. Noise-robust, physical microscopic deconvolution algorithm enabled by multi-resolution analysis regularization. Preprint atbioRxivhttps://doi.org/10.1101/2023.03.02.530744https://doi.org/10.1101/2023.03.02.530744(2023).

Orieux, F. et al. Bayesian estimation for optimized structured illumination microscopy.IEEE Trans. Image Process.21, 601–614, https://doi.org/10.1109/TIP.2011.2162741 (2012)..

Dong, S. Y. et al. Resolution doubling with a reduced number of image acquisitions.Biomed. Opt. Express6, 2946–2952, https://doi.org/10.1364/boe.6.002946 (2015)..

Ströhl, F.&Kaminski, C. F. Speed limits of structured illumination microscopy.Opt. Lett.42, 2511–2514, https://doi.org/10.1364/ol.42.002511 (2017)..

Lal, A. et al. A frequency domain SIM reconstruction algorithm using reduced number of images.IEEE Trans. Image Process.27, 4555–4570, https://doi.org/10.1109/TIP.2018.2842149 (2018)..

Zeng, H., Liu, G. X.&Zhao, R. SIM reconstruction framework for high-speed multi-dimensional super-resolution imaging.Opt. Express30, 10877–10898, https://doi.org/10.1364/oe.450136 (2022)..

Ma, Y. et al. Structured illumination microscopy with interleaved reconstruction (SIMILR).J. Biophotonics11, e201700090, https://doi.org/10.1002/jbio.201700090 (2018)..

Guo, Y. T. et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales.Cell175, 1430–1442.e17, https://doi.org/10.1016/j.cell.2018.09.057 (2018)..

Tokunaga, M., Imamoto, N.&Sakata-Sogawa, K. Highly inclined thin illumination enables clear single-molecule imaging in cells.Nat. Methods5, 159–161, https://doi.org/10.1038/nmeth1171 (2008)..

Gong, H., Guo, W. J.&Neil, M. A. A. GPU-accelerated real-time reconstruction in Python of three-dimensional datasets from structured illumination microscopy with hexagonal patterns.Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.379, 20200162, https://doi.org/10.1098/rsta.2020.0162 (2021)..

Markwirth, A. et al. Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction.Nat. Commun.10, 4315, https://doi.org/10.1038/s41467-019-12165-x (2019)..

Lin, S. Image reconstruction for structured illumination microscopy implemented in CUDA.https://github.com/iandobbie/CUDASIMreconhttps://github.com/iandobbie/CUDASIMrecon(2015).

Lu-Walther, H. W. et al. fastSIM: a practical implementation of fast structured illumination microscopy.Methods Appl. Fluoresc.3, 014001, https://doi.org/10.1088/2050-6120/3/1/014001 (2015)..

Cragg, G. E.&So, P. T. C. Lateral resolution enhancement with standing evanescent waves.Opt. Lett.25, 46–48, https://doi.org/10.1364/ol.25.000046 (2000)..

So, P. T. C., Kwon, H. S.&Dong, C. Y. Resolution enhancement in standing-wave total internal reflection microscopy: a point-spread-function engineering approach.J. Opt. Soc. Am.18, 2833–2845, https://doi.org/10.1364/JOSAA.18.002833 (2001)..

Manton, J. D. Answering some questions about structured illumination microscopy.Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.380, 20210109, https://doi.org/10.1098/rsta.2021.0109 (2022)..

Dan, D. et al. Rapid image reconstruction of structured illumination microscopy directly in the spatial domain.IEEE Photonics J.13, 3900411, https://doi.org/10.1109/JPHOT.2021.3053110 (2021)..

Wang, Z. J. et al. High-speed image reconstruction for optically sectioned, super-resolution structured illumination microscopy.Adv. Photonics4, 026003, https://doi.org/10.1117/1.Ap.4.2.026003 (2022)..

Wang, Z. J. et al. Rapid, artifact-reduced, image reconstructionfor super-resolution structured illumination microscopy.Innovation4, 100425, https://doi.org/10.1016/j.xinn.2023.100425 (2023)..

Tu, S. J. et al. Fast reconstruction algorithm for structured illumination microscopy.Opt. Lett.45, 1567–1570, https://doi.org/10.1364/OL.387888 (2020)..

Ball, G. et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy.Sci. Rep.5, 15915, https://doi.org/10.1038/srep15915 (2015)..

Culley, S. et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts.Nat. Methods15, 263–266, https://doi.org/10.1038/nmeth.4605 (2018)..

Koho, S. et al. Fourier ring correlation simplifies image restoration in fluorescence microscopy.Nat. Commun.10, 3103, https://doi.org/10.1038/s41467-019-11024-z (2019)..

Descloux, A., Grußmayer, K. S.&Radenovic, A. Parameter-free image resolution estimation based on decorrelation analysis.Nat. Methods16, 918–924, https://doi.org/10.1038/s41592-019-0515-7 (2019)..

Wang, Y. M. et al. The largest isoform of Ankyrin-G is required for lattice structure of the axon initial segment.Biochem. Biophys. Res. Commun.578, 28–34, https://doi.org/10.1016/j.bbrc.2021.09.017 (2021)..

Wang, Y. M. et al. Glucose increases the length and spacing of the lattice structure of the axon initial segment.Microsc. Res. Tech.85, 2679–2691, https://doi.org/10.1002/jemt.24122 (2022)..

Smith, C. S. et al. Structured illumination microscopy with noise-controlled image reconstructions.Nat. Methods18, 821–828, https://doi.org/10.1038/s41592-021-01167-7 (2021)..

Zhu, H. F. et al. 3D super-resolution microscopy based on nonlinear gradient descent structured illumination.Opt. Express29, 21428–21443, https://doi.org/10.1364/OE.424740 (2021)..

Cai, M. X. et al. Total variation and spatial iteration-based 3D structured illumination microscopy.Opt. Express30, 7938–7953, https://doi.org/10.1364/OE.451190 (2022)..

Cao, R. J. et al. Open-3DSIM: an open-source three-dimensional structured illumination microscopy reconstruction platform. Preprint atbioRxivhttps://doi.org/10.1101/2022.12.16.520543https://doi.org/10.1101/2022.12.16.520543(2022)..

Förster, R. et al. Motion artefact detection in structured illumination microscopy for live cell imaging.Opt. Express24, 22121–22134, https://doi.org/10.1364/oe.24.022121 (2016)..

Förster, R. et al. Automated distinction of shearing and distortion artefacts in structured illumination microscopy.Opt. Express26, 20680–20694, https://doi.org/10.1364/oe.26.020680 (2018)..

Mudry, E. et al. Structured illumination microscopy using unknown speckle patterns.Nat. Photonics6, 312–315, https://doi.org/10.1038/nphoton.2012.83 (2012)..

Min, J. H. et al. Fluorescent microscopy beyond diffraction limits using speckle illumination and joint support recovery.Sci. Rep.3, 2075, https://doi.org/10.1038/srep02075 (2013)..

Wipf, D. P.&Rao, B. D. An empirical bayesian strategy for solving the simultaneous sparse approximation problem.IEEE Trans. Signal Process.55, 3704–3716, https://doi.org/10.1109/TSP.2007.894265(2007)..

Ayuk, R. et al. Structured illumination fluorescence microscopy with distorted excitations using a filtered blind-SIM algorithm.Opt. Lett.38, 4723–4726, https://doi.org/10.1364/ol.38.004723 (2013)..

Zheng, G. A., Horstmeyer, R.&Yang, C. Wide-field, high-resolution Fourier ptychographic microscopy.Nat. Photonics7, 739–745, https://doi.org/10.1038/nphoton.2013.187 (2013)..

Dong, S. Y. et al. High-resolution fluorescence imaging via pattern-illuminated Fourier ptychography.Opt. Express22, 20856–20870, https://doi.org/10.1364/oe.22.020856 (2014)..

Dong, S. Y. et al. Incoherent Fourier ptychographic photography using structured light.Photonics Res.3, 19–23, https://doi.org/10.1364/PRJ.3.000019 (2015)..

Ströhl, F.&Kaminski, C. F. A joint Richardson—Lucy deconvolution algorithm for the reconstruction of multifocal structured illumination microscopy data.Methods Appl. Fluoresc.3, 014002, https://doi.org/10.1088/2050-6120/3/1/014002 (2015)..

Chakrova, N., Rieger, B.&Stallinga, S. Deconvolution methods for structured illumination microscopy.J. Opt. Soc. Am.33, B12–B20, https://doi.org/10.1364/JOSAA.33.000B12 (2016)..

Yeh, L. H., Tian, L.&Waller, L. Structured illumination microscopy with unknown patterns and a statistical prior.Biomed. Opt. Express8, 695–711, https://doi.org/10.1364/boe.8.000695 (2017)..

Liu, Q. L. et al. Total internal reflection fluorescence pattern-illuminated Fourier ptychographic microscopy.Opt. Lasers Eng.123, 45–52, https://doi.org/10.1016/j.optlaseng.2019.06.023 (2019)..

Samanta, K. et al. Blind super-resolution approach for exploiting illumination variety in optical-lattice illumination microscopy.ACS Photonics8, 2626–2634, https://doi.org/10.1021/acsphotonics.1c00503 (2021)..

Sarkar, S., Samanta, K.&Joseph, J. Method for single-shot fabrication of chiral woodpile photonic structures using phase-controlled interference lithography.Opt. Express28, 4347–4361, https://doi.org/10.1364/oe.384987 (2020)..

Agarwal, K.&Macháň, R. Multiple signal classification algorithm for super-resolution fluorescence microscopy.Nat. Commun.7, 13752, https://doi.org/10.1038/ncomms13752 (2016)..

Jost, A. et al. Optical sectioning and high resolution in single-slice structured illumination microscopy byThick sliceblind-SIM reconstruction.PLoS ONE10, e0132174, https://doi.org/10.1371/journal.pone.0132174 (2015)..

Negash, A. et al. Improving the axial and lateral resolution of three-dimensional fluorescence microscopy using random speckle illuminations.J. Opt. Soc. Am.33, 1089–1094, https://doi.org/10.1364/JOSAA.33.001089 (2016)..

Labouesse, S. et al. Joint reconstruction strategy for structured illumination microscopy with unknown illuminations.IEEE Trans. Image Process.26, 2480–2493, https://doi.org/10.1109/tip.2017.2675200 (2017)..

Soubies, E.&Unser, M. Computational super-sectioning for single-slice structured-illumination microscopy.IEEE Trans. Comput. Imaging5, 240–250, https://doi.org/10.1109/TCI.2018.2887136 (2019)..

Stephen, B. et al. Distributed optimization and statistical learning via the alternating direction method of multipliers.Found. Trends® Mach. Learn.3, 1–122, https://doi.org/10.1561/2200000016 (2011)..

Gregor, I. et al. Rapid nonlinear image scanning microscopy.Nat. Methods14, 1087–1089, https://doi.org/10.1038/nmeth.4467 (2017)..

Ingaramo, M. et al. Two-photon excitation improves multifocal structured illumination microscopy in thick scattering tissue.Proc. Natl Acad. Sci. USA111, 5254–5259, https://doi.org/10.1073/pnas.1314447111 (2014)..

Winter, P. W. et al. Two-photon instant structured illumination microscopy improves the depth penetration of super-resolution imaging in thick scattering samples.Optica1, 181–191, https://doi.org/10.1364/optica.1.000181 (2014)..

Wu, Y. C.&Shroff, H. Faster, sharper, and deeper: structured illumination microscopy for biological imaging.Nat. Methods15, 1011–1019, https://doi.org/10.1038/s41592-018-0211-z (2018)..

Chang, B. J. et al. csiLSFM combines light-sheet fluorescence microscopy and coherent structured illumination for a lateral resolution below 100 nm.Proc. Natl Acad. Sci. USA114, 4869–4874, https://doi.org/10.1073/pnas.1609278114 (2017)..

Classen, A. et al. Superresolution via structured illumination quantum correlation microscopy.Optica4, 580–587, https://doi.org/10.1364/OPTICA.4.000580 (2017)..

Zhao, G. Y. et al. Resolution-enhanced SOFI via structured illumination.Opt. Lett.42, 3956–3959, https://doi.org/10.1364/OL.42.003956 (2017)..

Classen, A., von Zanthier, J.&Agarwal, G. S. Analysis of super-resolution via 3D structured illumination intensity correlation microscopy.Opt. Express26, 27492–27503, https://doi.org/10.1364/oe.26.027492 (2018)..

Wang, Y. F. et al. Combined expansion microscopy with structured illumination microscopy for analyzing protein complexes.Nat. Protoc.13, 1869–1895, https://doi.org/10.1038/s41596-018-0023-8 (2018)..

Helle, Ø. I. et al. Structured illumination microscopy using a photonic chip.Nat. Photonics14, 431–438, https://doi.org/10.1038/s41566-020-0620-2 (2020)..

Pilger, C. et al. Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope.Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.379, 20200300, https://doi.org/10.1098/rsta.2020.0300 (2021)..

Wang, L. et al. Improvement in resolution of multiphoton scanning structured illumination microscopy via harmonics.Engineering16, 65–72, https://doi.org/10.1016/j.eng.2021.12.010 (2022)..

de Haan, K., Rivenson, Y., Wu, Y. C.&Ozcan, A. Deep-learning-based image reconstruction and enhancement in optical microscopy.Proc. IEEE108, 30–50, https://doi.org/10.1109/JPROC.2019.2949575 (2020)..

Belthangady, C.&Royer, L. A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction.Nat. Methods16, 1215–1225, https://doi.org/10.1038/s41592-019-0458-z (2019)..

Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy.Nat. Methods16, 103–110, https://doi.org/10.1038/s41592-018-0239-0 (2019)..

Jin, L. H. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed.Nat. Commun.11, 1934, https://doi.org/10.1038/s41467-020-15784-x (2020)..

Qiao, C. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy.Nat. Methods18, 194–202, https://doi.org/10.1038/s41592-020-01048-5 (2021)..

Shah, Z. H. et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images.Photonics Res.9, B168–B181, https://doi.org/10.1364/PRJ.416437 (2021)..

Christensen, C. N. et al. ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning.Biomed. Opt. Express12, 2720–2733, https://doi.org/10.1364/boe.414680 (2021)..

Xypakis, E. et al. Deep learning for blind structured illumination microscopy.Sci. Rep.12, 8623, https://doi.org/10.1038/s41598-022-12571-0 (2022)..

Zhang, Y. D. et al. ActiveStereoNet: end-to-end self-supervised learning for active stereo systems. InProc. 15th European Conference on Computer Vision802–819 (Springer, 2018).

Boland, M. A. et al. Improving axial resolution in structured illumination microscopy using deep learning.Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.379, 20200298, https://doi.org/10.1098/rsta.2020.0298 (2021)..

Ling, C. et al. Fast structured illumination microscopy via deep learning.Photonics Res.8, 1350–1359, https://doi.org/10.1364/PRJ.396122 (2020)..

Qiao, C. et al. 3D structured illumination microscopy via channel attention generative adversarial network.IEEE J. Sel. Top. Quantum Electron.27, 6801711, https://doi.org/10.1109/JSTQE.2021.3060762 (2021)..

Zhang, Q. N. et al. Deep learning-based single-shot structured illumination microscopy.Opt. Lasers Eng.155, 107066, https://doi.org/10.1016/j.optlaseng.2022.107066 (2022)..

Cheng, X. et al. Fast and lightweight network for single frame structured illumination microscopy super-resolution.IEEE Trans. Instrum. Meas.71, 5007711, https://doi.org/10.1109/TIM.2022.3161721 (2022)..

Qiao, C. et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.Nat. Biotechnol.41, 367–377, https://doi.org/10.1038/s41587-022-01471-3 (2023)..

Airy Polar-SIM.https://www.airy-tech.cnhttps://www.airy-tech.cn(2022).

N-SIM S.https://www.microscope.healthcare.nikon.com/products/super-resolution-microscopes/n-sim-shttps://www.microscope.healthcare.nikon.com/products/super-resolution-microscopes/n-sim-s(2022).

DeltaVision OMX SR.https://imaging.wehi.edu.au/our-instruments/deltavision-omx-srhttps://imaging.wehi.edu.au/our-instruments/deltavision-omx-sr(2022).

HiS-SIM.http://www.csr-biotech.com/product_his_simhttp://www.csr-biotech.com/product_his_sim(2022).

Multi-SIM.https://www.naxi-tech.com/https://www.naxi-tech.com/(2022).

ZEISS product information Elyra-7.https://www.zeiss.com/microscopy/en/about-us/newsroom/press-releases/2021/zeiss-elyra-7.htmlhttps://www.zeiss.com/microscopy/en/about-us/newsroom/press-releases/2021/zeiss-elyra-7.html(2022).

Zhanghao, K. et al. Super-resolution imaging of fluorescent dipoles via polarized structured illumination microscopy.Nat. Commun.10, 4694, https://doi.org/10.1038/s41467-019-12681-w (2019)..

Strack, R. Deep learning advances super-resolution imaging.Nat. Methods15, 403, https://doi.org/10.1038/s41592-018-0028-9 (2018)..

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