Journal
OPTICA
Volume 5, Issue 4, Pages 458-464Publisher
Optica Publishing Group
DOI: 10.1364/OPTICA.5.000458
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Funding
- Zuckerman Foundation
- Technion-Israel Institute of Technology
- Ollendorf Foundation
- Taub Foundation
- Israel Science Foundation (ISF) [852/17]
- Israel Academy of Sciences and Humanities
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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. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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