Yakov Pchelintsev
ABSTRACT
A probabilistic approach for super-resolution of blinking fluorescence microscopy was suggested. Its performance was compared with modern blinking fluorescence image enhancement algorithms, namely SOFI, MUSICAL and SPARCOM in different conditions. The comparison was performed using both synthetic and real experimental data.
- Dertinger, T., Colyer, R., Iyer, G., Weiss, S. and Enderlein, J. 2009. Fast, background-free, 3D super-resolution optical fluctuation imaging (SOFI). Proceedings of the National Academy of Sciences. 106, 52 (2009), 22287--22292. DOI= https://doi.org/10.1073/pnas.0907866106.Google Scholar
Cross Ref
- Mendel, J. 1991. Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE. 79, 3 (1991), 278--305. DOI= https://doi.org/10.1109/5.75086.Google Scholar
- Dertinger, T., Colyer, R., Vogel, R., Enderlein, J. and Weiss, S. 2010. Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI). Optics Express. 18, 18 (2010), 18875. DOI= https://doi.org/10.1364/oe.18.018875.Google ScholarCross Ref
- Solomon, O., Mutzafi, M., Segev, M. and Eldar, Y. 2018. Sparsity-based super-resolution microscopy from correlation information. Optics Express. 26, 14 (2018), 18238. DOI= https://doi.org/10.1364/OE.26.018238.Google ScholarCross Ref
- Agarwal, K. and Macháň, R. 2016. Multiple signal classification algorithm for super-resolution fluorescence microscopy. Nature Communications. 7, 1 (2016), 13752. DOI= https://doi.org/10.1038/ncomms13752.Google ScholarCross Ref
- Schmidt, R. 1986. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation. 34, 3 (1986), 276--280. DOI= https://doi.org/10.1109/TAP.1986.1143830.Google Scholar
- Cox, S., Rosten, E., Monypenny, J., Jovanovic-Talisman, T., Burnette, D., Lippincott-Schwartz, J., Jones, G. and Heintzmann, R. 2011. Bayesian localization microscopy reveals nanoscale podosome dynamics. Nature Methods. 9, 2 (2011), 195--200. DOI= https://doi.org/10.1038/nmeth.1812.Google ScholarCross Ref
- Nehme, E., Weiss, L., Michaeli, T. and Shechtman, Y. 2018. Deep-STORM: Super-resolution single-molecule microscopy by deep learning. Optica. 5, 4 (2018), 458. DOI= https://doi.org/10.1364/OPTICA.5.000458.Google Scholar
- Li, Y., Xu, F., Zhang, F., Xu, P., Zhang, M., Fan, M., Li, L., Gao, X. and Han, R. 2018. DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy. Bioinformatics. 34, 13 (2018), i284--i294. DOI= https://doi.org/10.1093/bioinformatics/bty241.Google ScholarCross Ref
- Dempster, A., Laird, N. and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological). 39, 1 (1977), 1--22. DOI=https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.Google Scholar
- Bishop, C. 2006. Pattern recognition and machine learning. Springer.Google Scholar
- Richardson, W. 1972. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America. 62, 1 (1972), 55. DOI= https://doi.org/10.1364/JOSA.62.000055.Google Scholar
- Lucy, L. 1974. An iterative technique for the rectification of observed distributions. The Astronomical Journal. 79, (1974), 745. DOI= https://doi.org/10.1086/111605.Google ScholarCross Ref
- Sage, D., Kirshner, H., Pengo, T., Stuurman, N., Min, J., Manley, S. and Unser, M. 2015. Quantitative evaluation of software packages for single-molecule localization microscopy. Nature Methods. 12, 8 (2015), 717--724. DOI= https://doi.org/10.1038/nmeth.3442.Google ScholarCross Ref
- Sage, D. et al. 2019. Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software. Nature Methods. 16, 5 (2019), 387--395. DOI= https://doi.org/10.1038/s41592-019-0364-4.Google ScholarCross Ref
- Girsault, A., Lukes, T., Sharipov, A., Geissbuehler, S., Leutenegger, M., Vandenberg, W., Dedecker, P., Hofkens, J. and Lasser, T. 2016. SOFI Simulation Tool: A software package for simulating and testing Super-Resolution Optical Fluctuation Imaging. PLOS ONE. 11, 9 (2016), e0161602. DOI= https://doi.org/10.1371/journal.pone.0161602.Google ScholarCross Ref
- Weisshart, K. 2014. The basic principle of Airyscanning. Technology Note. Zeiss.Google Scholar
- Uno, S., Kamiya, M., Yoshihara, T., Sugawara, K., Okabe, K., Tarhan, M., Fujita, H., Funatsu, T., Okada, Y., Tobita, S. and Urano, Y. 2014. A spontaneously blinking fluorophore based on intramolecular spirocyclization for live-cell super-resolution imaging. Nature Chemistry. 6, 8 (2014), 681--689. DOI= https://doi.org/10.1038/nchem.2002.Google ScholarCross Ref
- Sheppard, C., Mehta, S. and Heintzmann, R. 2013. Superresolution by image scanning microscopy using pixel reassignment. Optics Letters. 38, 15 (2013), 2889. DOI= https://doi.org/10.1364/OL.38.002889.Google ScholarCross Ref
Index Terms
- Enhancement Algorithms for Blinking Fluorescence Imaging
Recommendations
Adaptive Image Enhancement for Fluorescence Microscopy
TAAI '10: Proceedings of the 2010 International Conference on Technologies and Applications of Artificial IntelligenceFluorescent cell micrographs contain inhomogeneous contrast levels due to fluorescence intensity variations and the existence of out-of-focus objects. A novel image enhancement method based on adaptive local region sizes is proposed, which can correctly ...
A contrast enhancement method using dynamic range separate histogram equalization
In this paper, a contrast enhancement method using dynamic range separate histogram equalization (DRSHE) is proposed. Histogram equalization (HE) method is widely used for contrast enhancement. However, HE is not suitable for consumer electronic ...
Comments