ru/ ru

ISSN: 1023-5086


ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

Article submission Подать статью
Больше информации Back

DOI: 10.17586/1023-5086-2023-90-01-49-59

УДК: 535.4

Super-resolution microscopy based on denoised conventional raw images with wide spectrum denoising

For Russian citation (Opticheskii Zhurnal):

Cheng T. Super-resolution microscopy based on denoised conventional raw images with wide spectrum denoising (Микроскопия высокого разрешения на основе необработанных изображений с шумоподавлением в широкой полосе частот) [на англ. яз.] // Оптический журнал. 2023. Т. 90. № 1. С. 49–59.


Cheng T. Super-resolution microscopy based on denoised conventional raw images with wide spectrum denoising (Микроскопия высокого разрешения на основе необработанных изображений с шумоподавлением в широкой полосе частот) [in English] // Opticheskii Zhurnal. 2023. V. 90. № 1. P. 49–59.

For citation (Journal of Optical Technology):

Tao Cheng, "Super-resolution microscopy based on denoised conventional raw images with wide spectrum denoising," Journal of Optical Technology. 90(1), 26-32 (2023).


Subject of study is a scheme of screening better super-resolution reconstruction based on denoising the conventional raw image with wide spectrum denoising. Purpose of the work. Improving the reconstruction effect of ultra-high-resolution images, for this, reconstruction with noise reduction and compression of ordinary raw images and high-resolution images has been investigated. Method. The binned high resolution raw image is the conventional raw image. Conventional raw images and high resolution raw images were denoised with wide spectrum denoising respectively. The conventional raw images and high resolution raw images before and after denoising are reconstructed by compressed sensing. Main Results. The denoising ability of wide spectrum denoising based on high-resolution raw images is very stable and does not change with molecular density. The signal to noise ratios improves by approximately 8 dB. The denoising ability of wide spectrum denoising based on conventional raw images is not good. The signal to noise ratios of conventional raw images is 6 dB higher than that of high resolution raw images. The signal to noise ratios of denoised high resolution and conventional raw images are almost the same. Compressed sensing reconstruction of the denoised conventional raw images is inferior to that of denoised high-resolution raw images, however, is better than that of high-resolution and conventional raw images. Practical significance. In the conventional single-molecule localizations and super-resolution microscopy, the pixel size of a raw image is about equal to the standard deviation of the point spread function. Wide spectrum denoising can improve the conventional raw image denoising and reconstruction. However, better super-resolution microscopy can be achieved based on wide spectrum denoising and high-resolution raw images. Super-resolution microscopy of high-resolution raw images will become a new research point.


Acknowledgment: the study was funded by the Natural Science Foundation of Guangxi Province (2022GXNSFAA035593) and National Natural Science Foundation of China (81660296, 41461082).


super-resolution microscopy, pixel size, standard deviation, point spread function, compressed sensing

OCIS codes: 170.2520, 100.6640, 050.1960

  1. Khater M., Nabi L.R., Hamarneh G. A review of super­resolution single­molecule localization microscopy cluster analysis and quantification methods // Patterns. 2020. V. 1. № 3. P. 1–23.
  2. Lelek M., Gyparaki M.T., Beliu G., Schueder F., Griffié J., Manley S., Jungmann R., Sauer M., Lakadamyali M., Zimmer C. Single­molecule localization microscopy // Nat. Rev. Methods Primers. 2012. V. 1. № 39. P. 1–27.­021­00038­ x
  3. Zhu L., Zhang W., Elnatan D., Huang B. Faster STORM using compressed sensing // Nat. Methods. 2012. V. 9. № 7. P. 721–723.
  4. Cheng T., Chen D.N., Yu B., Niu H.B. Reconstruction of super­resolution STORM images using compressed sensing based on low­resolution raw images and interpolation // Biomed. Opt. Exp. 2017. V. 8. № 5. P. 2445–2457.
  5. Valli J., Garcia­Burgos A., Rooney L.M., de Melo e Oliveira B.V., Duncan R.R., Rickman C. Seeing beyond the limit: A guide to choosing the right super­resolution microscopy technique // J. Biol. Chem. 2021. V. 297. № 1. P. 1–13.
  6. Nizamudeena Z., Markusb R., Lodgec R., Parmenterd C., Platte M., Chakrabartif L., Sottilea V. Rapid and accurate analysis of stem cell­derived extracellular vesicles with super resolution microscopy and live imaging // BBA – Mol. Cell. Res. 2018. V. 1865. № 12. P. 1891–1900.
  7. Achimovich A.M., Ai H., Gahlmann A. Enabling technologies in super­resolution fluorescence microscopy: reporters, labeling, and methods of measurement // Curr. Opin. Struct. Biol. 2019. V. 58. № 10. P. 224–232.
  8. Thompson R.E., Larson D.R., Webb W.W. Precise nanometer localization analysis for individual fluorescent probes // Biophys. J. 2002. V. 82. № 5. P. 2775–2783.­3495(02)75618­X
  9. Cheezum M.K., Walker W.F., Guilford W.H. Quantitative comparison of algorithms for tracking single fluorescent particles // Biophys. J. 2001. V. 81. № 5. P. 2378–2388.­3495(01)75884­5
  10. Henriques R., Lelek M., Fornasiero E.F., Valtorta F., Zimmer C., Mhlanga M.M. QuickPALM: 3D real­time photoactivation nanoscopy image processing in ImageJ // Nat. Methods. 2010. V. 7. № 5. P. 339–340.­339
  11. Cox S., Rosten E., Monypenny J., Jovanovic­Talisman T., Burnette D.T., Lippincott­Schwartz J., Jones G.E., Heintzmann R. Bayesian localization microscopy reveals nanoscal`e podosome dynamics // Nat. Methods. 2011. V. 9. № 2. P. 195–200.
  12. Burnettea D.T., Senguptaa P., Daib Y., Lippincott­Schwartza J., Kacharb B. Bleaching/blinking assisted localization microscopy for superresolution imaging using standard fluorescent molecules // PNAS. 2011. V. 108. № 52. P. 21081–21086.
  13. Calisesi G., Ghezzi A., Ancora D., D'Andrea C., Valentini G., Farina A., Bassi A. Compressed sensing in fluorescence microscopy // Prog. Biophys. Mol. Bio. 2021. V. 168. № 1. P. 66–80.
  14. Arjoune Y., Kaabouch N., Ghazi H.E., Tamtaoui A. A performance comparison of measurement matrices in compressive sensing // Int. J. Commun. Syst. 2018. V. 31. № 10. P. 1–18.
  15. Holden S.J., Uphoff S., Kapanidis A.N. DAOSTORM: An algorithm for high­density super­resolution microscopy // Nat. Methods. 2011. V. 8. № 4. P. 279–280.­279
  16. Min J., Vonesch C.,  Carlini L., KirshnerH., et al.  FALCON: Fast and unbiased reconstruction of high­density super­resolution microscopy data // Sci. Reports. 2014. V. 4. № 4. P. 1–9.
  17. Li Y., Mund M., Hoess P., Deschamps J., et al. Real­time 3D single­molecule localization using experimental point spread functions // Nat. Methods. 2018. V. 15. № 4. P. 367–369.
  18. Cheng T., Chen D.N., and Li H. Wide spectrum denoising (WSD) for superresolution microscopy imaging using compressed sensing and a high­resolution camera // J. Phys.: Conf. Ser. (2020 Internat. Conf. Computer Vision and Data Mining) 2020. V. 1651. № 1. P. 1–13.­6596/1651/1/012177
  19. Cheng T. Wide spectrum denoising method for microscopic images // US Patent 16845110. July 30, 2020.
  20. Beier H.T., Ibey B.L. Experimental comparison of the high­speed imaging performance of an EM­CCD and sCMOS camera in a dynamic live­cell imaging test case // PLOS ONE. 2014. V. 9. № 1. P. 1–6.
  21. Sage D., Kirshner Н., Pengo Т., Stuurman N.,et al. Quantitative evaluation of software packages for single­molecule localization microscopy // Nat. Methods. 2014. V. 12. № 8. P. 717–724.
  22. Roa C., Le V.N.D., Mahendroo M., Saytashev I., Ramella­Roman J.C. Auto­detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K­NN and semantic segmentation classification // Biomed. Opt. Exp. 2021. V. 12. № 4. P. 2236–2249.