%0 Journal Article %1 Nehme2018 %A Nehme, Elias %A Weiss, Lucien E. %A Michaeli, Tomer %A Shechtman, Yoav %D 2018 %I Optical Society of America %J Optica %K Diffraction Software aperture enhancement,Image limit,High numerical optics,Image processing,Image reconstruction,Spatial resolution %N 4 %P 458 %R 10.1364/OPTICA.5.000458 %T Deep-STORM: super-resolution single-molecule microscopy by deep learning %U https://www.osapublishing.org/abstract.cfm?URI=optica-5-4-458 %V 5 %X We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking dataset. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.

@article{Nehme2018, abstract = {We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking dataset. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.}, added-at = {2020-03-23T21:12:34.000+0100}, author = {Nehme, Elias and Weiss, Lucien E. and Michaeli, Tomer and Shechtman, Yoav}, biburl = {https://www.bibsonomy.org/bibtex/205b8b2c9ebee8907d259e32606fe13ff/kfriedl}, doi = {10.1364/OPTICA.5.000458}, file = {:C$\backslash$:/Users/Karoline/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Nehme et al. - 2018 - Deep-STORM super-resolution single-molecule microscopy by deep learning.pdf:pdf}, interhash = {796a1601440d313145313ee3441620d3}, intrahash = {05b8b2c9ebee8907d259e32606fe13ff}, issn = {2334-2536}, journal = {Optica}, keywords = {Diffraction Software aperture enhancement,Image limit,High numerical optics,Image processing,Image reconstruction,Spatial resolution}, month = apr, number = 4, pages = 458, publisher = {Optical Society of America}, timestamp = {2020-03-23T21:37:38.000+0100}, title = {{Deep-STORM: super-resolution single-molecule microscopy by deep learning}}, url = {https://www.osapublishing.org/abstract.cfm?URI=optica-5-4-458}, volume = 5, year = 2018 }