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Super-resolution microscopy based on wide spectrum denoising and compressed sensing
T. Cheng 1, H. Jin 1

1 Guangxi University of Science and Technology,
545006, Liuzhou, P. R. China, Chengzhong District, Avenue Donghuan 268

 PDF, 2350 kB

DOI: 10.18287/2412-6179-CO-1172

Страницы: 426-432.

Язык статьи: English.

Аннотация:
WSD can effectively remove random noise of a raw image from very low density to ultra-high density fluorescent molecular distribution scenarios. The size of the raw image that WSD can denoise is subject to the used measurement matrix. A large raw image must be divided into blocks so that WSD denoises each block separately. Based on traditional single-molecule localization and super-resolution reconstruction scenarios, wide spectrum denoising (WSD) for blocks of different sizes was studied. The denoising ability is related to block sizes. The general trend is when the block gets larger, the denoising effect gets worse. When the block size is equal to 10, the denoising effect is the best. Using compressed sensing, only 20 raw images are needed for reconstruction. The temporal resolution is less than half a second. The spatial resolution is also greatly improved.

Ключевые слова:
fluorescence microscopy, super-resolution, noise, diffraction theory, compressed sensing.

Благодарности
The work was funded by Guangxi National Natural Science Foundation (2022GXNSFAA035593), National Natural Science Foundation of China (81660296, 41461082).

Цитирование:
Cheng, T. Super-resolution microscopy based on wide spectrum denoising and compressed sensing / T. Cheng, H. Jin //Computer Optics. - 2023. - Vol. 47(3). - P. 426-432. - DOI: 10.18287/2412-6179-CO-1172.

Citation:
Cheng T, Jin H. Super-resolution microscopy based on wide spectrum denoising and compressed sensing. Computer Optics 2023; 47(3): 426-432. DOI: 10.18287/2412-6179-CO-1172.

References:
  1. Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S, Bonifacino JS,Davidson MW, Lippincott-Schwartz J, Hess HF, Imaging intracellular fluorescent proteins at nanometer resolution, Science 2006; 313(15): 1642-1645. DOI: 10.1126/science.1127344.
  2. Rust MJ , Bates M, Zhuang XW, Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM), Nature methods 2006; 3(10) :793-796. DOI:10.1038/nmeth929.
  3. Komis G, Samajová O, Ovečka M, Samaj J, Super-resolution Microscopy in Plant Cell Imaging, Trends in Plant Science 2015; 20 (12):834-843. DOI: 10.1016/j.tplants.2015.08.013.
  4. Nizamudeena Z, Markusb R, Lodgec R, Parmenterd C, Platte M, Chakrabartif L, Sottilea V, Rapid and accurate analysis of stem cell-derived extracellular vesicles with super resolution microscopy and live imaging, BBA - Molecular Cell Research (2018); 76 (1865):1891–1900. DOI:10.1016/j.bbamcr.2018.09.008.
  5. Achimovich AM, Ai H, Gahlmann A, Enabling technologies in super-resolution fluorescence microscopy: reporters, labeling, and methods of measurement, Current Opinion in Structural Biology (2019); 32 (58): 224–232. DOI:10.1016/j.sbi.2019.05.001.
  6. Valli J, Garcia-Burgos A, Rooney LM, Oliveira BVdMe, Duncan RR, Rickman C, Seeing beyond the limit: A guide to choosing the right super-resolution microscopy technique, Journal of Biological Chemistry (2021); 297(1): 1-13 . DOI:10.1016/j.jbc.2021.100791.
  7. Calises Gi, Ghezzi A, Ancora D, D'Andrea C, Valentini G, Farina A, Bassi A, Compressed sensing in fluorescence microscopy, Progress in Biophysics and Molecular Biology (2022); 60 (168) :66-80. DOI：10.1016/j.pbiomolbio.2021.06.004.
  8. Thompson RE, Larson DR, Webb WW, Precise Nanometer Localization Analysis for Individual Fluorescent Probes, Biophysical Journal (2002); 63 (82) :2775-2783. DOI：10.1016/S0006-3495(02)75618-X.
  9. Zhu L, Zhuang Wei, Elnatan D, Huang B, Faster STORM using compressed sensing, Nature methods (2102), 9(7):721-723. DOI: 10.1038/nmeth.1978.
  10. Cheezum MK, Walker WF, Guilford WH, Quantitative Comparison of Algorithms for Tracking Single Fluorescent Particles, Biophysical Journal（2001）;81(4):2378–2388. DOI:10.1016/S0006-3495(01)75884-5.
  11. Danie S, Hagai K, Thomas P, Nico S, Junhong M, Suliana M, Michael U, Quantitative evaluation of software packages for single-molecule localization microscopy, Nature Methods(2015); 12(8):717-724. DOI:10.1038/nmeth.3442.
  12. Holden SJ, Uphoff S, Kapanidis AN, DAOSTORM: an algorithm for high- density super-resolution microscopy, Nature Methods(2011);8 (4):279-280. DOI:10.1038/nmeth0411-279.
  13. Beier HT, Ibey BL, Experimental comparison of the high-speed imaging performance of an EM-CCD and SCMOS camera in a dynamic live-cell imaging test case, PLOS ONE(2014); 9(1) :1-6. DOI:10.1371/journal.pone.0084614.
  14. Min JH, Vonesch C, Kirshner H, Carlini L, Olivier. N, Holden. S, Manley S, Ye.JC, Unser M, FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data, Scientific reports(2014); 12 (4577):1-9. DOI:10.1038/srep04577.
  15. Wöll D, Flors C, Super-resolution Fluorescence Imaging for Materials Science, Small Methods(2017); 1(1700191): 1-12. DOI: 0.1002/smtd.201700191.
  16. Cheng T, Chen DN, Yu B, Niu HB, Reconstruction of super-resolution STORM images using compressed sensing based on low-resolution raw images and interpolation, Biomedical Optics Express(2017), 8(5) :2445-2457.DOI :10.1364/BOE.8.002445.
  17. Cheng T, Chen DN, Li H, Wide spectrum denoising (WSD) for superresolution microscopy imaging using compressed sensing and a high-resolution camera, Journal of Physics: Conference Series (2020 International Conference on Computer Vision and Data Mining), 1651 (2020) 012177. DOI:10.1088/1742-6596/1651/1/012177.
  18. Cheng T. Wide spectrum denoising method for microscopic images. US Patent 16845110 of July 2, 2022.
  19. Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL)，Lausanne, Benchmarking of Single-Molecule Localization Microscopy Software, Source: <http://bigwww.epfl.ch/smlm/>.
  20. Li YM, Mund  M, Hoess. P, Deschamps J, Matti U, Nijmeijer. B, Jimenez V, Ellenberg J, Ries. J, Real-time 3D single-molecule localization using experimental point spread functions, Nature Methods(2018);19 (15) : 367-369. DOI:10.1038/nmeth.4661.
  21. Elad M, Optimized projections for compressed sensing, IEEE Trans. Signal Process(2007); 55 (12):5695–5702. DOI:10.1109/TSP.2007.900760.
  22. Roa C, Le VND, Mahendroo M, Saytashev I, Ramella-Roman JC, Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using, Biomedical Optics Express(2021);12 (4):2236-2249. DOI:10.1364/BOE.420079.
  23. Lee G, Oh J-W, Her N-G, JeongW-K, DeepHCS++ : Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening, Medical Image Analysis(2021); 70 (101995)1-16. DOI:10.1016/j.media.2021.101995.

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