[1] Gustafsson, M. G. L. et al. Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination. Biophys. J. 94, 4957-4970 (2008). doi: 10.1529/biophysj.107.120345
[2] Heintzmann, R. & Huser, T. Super-resolution structured illumination microscopy. Chem. Rev. 117, 13890-13908 (2017). doi: 10.1021/acs.chemrev.7b00218
[3] Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. Cell Biol. 18, 685-701 (2017). doi: 10.1038/nrm.2017.71
[4] Wu, Y. C. & Shroff, H. Faster, sharper, and deeper: structured illumination microscopy for biological imaging. Nat. Methods 15, 1011-1019 (2018). doi: 10.1038/s41592-018-0211-z
[5] Kner, P. et al. Super-resolution video microscopy of live cells by structured illumination. Nat. Methods 6, 339-342 (2009). doi: 10.1038/nmeth.1324
[6] Hirvonen, L. M. et al. Structured illumination microscopy of a living cell. Eur. Biophys. J. 38, 807-812 (2009). doi: 10.1007/s00249-009-0501-6
[7] Li, D. et al. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics. Science 349, aab3500 (2015). doi: 10.1126/science.aab3500
[8] Huang, X. S. et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat. Biotechnol. 36, 451-459 (2018). doi: 10.1038/nbt.4115
[9] Guo, Y. T. et al. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell 175, 1430-1442 (2018). e17. doi: 10.1016/j.cell.2018.09.057
[10] Dong, D. S. et al. Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome. Light 9, 11 (2020). doi: 10.1038/s41377-020-0249-4
[11] Markwirth, A. et al. Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction. Nat. Commun. 10, 4315 (2019). doi: 10.1038/s41467-019-12165-x
[12] Sahl, S. J. et al. Comment on "Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics". Science 352, 527 (2016). doi: 10.1126/science.aad7983
[13] Hoffman, D. P. & Betzig, E. Tiled reconstruction improves structured illumination microscopy. Preprint at bioRxiv https://doi.org/10.1101/895318">https://doi.org/10.1101/895318 (2020).
[14] Schaefer, L. H., Schuster, D. & Schaffer, J. Structured illumination microscopy: artefact analysis and reduction utilizing a parameter optimization approach. J. Microsc. 216, 165-174 (2004). doi: 10.1111/j.0022-2720.2004.01411.x
[15] Pospíšil, J., Fliegel, K. & Klíma, M. Applications of Digital Image Processing XL (SPIE, 2017).
[16] Young, L. J., Ströhl, F. & Kaminski, C. F. A guide to structured illumination TIRF microscopy at high speed with multiple colors. J. Vis. Exp. 111, e53988 (2016). doi: 10.3791/53988
[17] Ball, G. et al. SIMcheck: a toolbox for successful super-resolution structured illumination microscopy. Sci. Rep. 5, 15915 (2015). doi: 10.1038/srep15915
[18] Demmerle, J. et al. Strategic and practical guidelines for successful structured illumination microscopy. Nat. Protoc. 12, 988-1010 (2017). doi: 10.1038/nprot.2017.019
[19] Kraus, F. et al. Quantitative 3D structured illumination microscopy of nuclear structures. Nat. Protoc. 12, 1011-1028 (2017). doi: 10.1038/nprot.2017.020
[20] Wicker, K. et al. Phase optimisation for structured illumination microscopy. Opt. Express 21, 2032-2049 (2013). doi: 10.1364/OE.21.002032
[21] Wicker, K. Non-iterative determination of pattern phase in structured illumination microscopy using auto-correlations in Fourier space. Opt. Express 21, 24692-24701 (2013). doi: 10.1364/OE.21.024692
[22] Zhou, X. et al. Image recombination transform algorithm for superresolution structured illumination microscopy. J. Biomed. Opt. 21, 096009 (2016). doi: 10.1117/1.JBO.21.9.096009
[23] Cao, R. Z. et al. Inverse matrix based phase estimation algorithm for structured illumination microscopy. Biomed. Opt. Express 9, 5037-5051 (2018). doi: 10.1364/BOE.9.005037
[24] Sola-Pikabea, J. et al. Fast and robust phase-shift estimation in two-dimensional structured illumination microscopy. PLoS ONE 14, e0221254 (2019). doi: 10.1371/journal.pone.0221254
[25] Chu, K. Q. et al. Image reconstruction for structured-illumination microscopy with low signal level. Opt. Express 22, 8687-8702 (2014). doi: 10.1364/OE.22.008687
[26] 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 (2016). doi: 10.1038/srep37149
[27] Karras, C. et al. Successful optimization of reconstruction parameters in structured illumination microscopy - a practical guide. Opt. Commun. 436, 69-75 (2019). doi: 10.1016/j.optcom.2018.12.005
[28] Jin, L. H. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat. Commun. 11, 1934 (2020). doi: 10.1038/s41467-020-15784-x
[29] Christensen, C. N. et al. ML-SIM: a deep neural network for reconstruction of structured illumination microscopy images. Preprint at https://arxiv.org/abs/2003.11064">https://arxiv.org/abs/2003.11064 (2020).
[30] Křížek, P. et al. SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy. Bioinformatics 32, 318-320 (2016). doi: 10.1093/bioinformatics/btv576
[31] Müller, M. et al. Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ. Nat. Commun. 7, 10980 (2016). doi: 10.1038/ncomms10980
[32] Lal, A., Shan, C. Y. & Xi, P. Structured illumination microscopy image reconstruction algorithm. IEEE J. Sel. Top. Quantum Electron. 22, 6803414 (2016). doi: 10.1109/JSTQE.2016.2521542
[33] Heintzmann, R. & Cremer, C. G. Optical Biopsies and Microscopic Techniques III (SPIE, 1999).
[34] Gustafsson, M. G. L. Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc. 198, 82-87 (2000). doi: 10.1046/j.1365-2818.2000.00710.x
[35] Lukeš, T. et al. Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation. Opt. Express 22, 29805-29817 (2014). doi: 10.1364/OE.22.029805
[36] O'Holleran, K. & Shaw, M. Optimized approaches for optical sectioning and resolution enhancement in 2D structured illumination microscopy. Biomed. Opt. Express 5, 2580-2590 (2014). doi: 10.1364/BOE.5.002580
[37] Lai-Tim, Y. et al. Jointly super-resolved and optically sectioned Bayesian reconstruction method for structured illumination microscopy. Opt. Express 27, 33251-33267 (2019). doi: 10.1364/OE.27.033251
[38] Johnson, K. A. & Hagen, G. M. Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction. GigaScience 9, giaa035 (2020). doi: 10.1093/gigascience/giaa035
[39] Shabani, H. et al. Optical transfer function engineering for a tunable 3D structured illumination microscope. Opt. Lett. 44, 1560-1563 (2019). doi: 10.1364/OL.44.001560
[40] Manton, J. D. et al. Concepts for structured illumination microscopy with extended axial resolution through mirrored illumination. Biomed. Opt. Express 11, 2098-2108 (2020). doi: 10.1364/BOE.382398
[41] Ingerman, E. A., London, R. A. & Gustafsson, M. G. L. Signal, noise and resolution in linear and nonlinear structured-illumination microscopy. J. Microsc. 273, 3-25 (2019). doi: 10.1111/jmi.12753
[42] Chen, B. C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014). doi: 10.1126/science.1257998