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Multicolor localization microscopy and point-spread-function engineering by deep learning
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DOI：10.1364/OE.27.006158.png)
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Abstract
En 中文
Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we apply deep learning to microscopy and demonstrate how neural networks can exploit the chromatic dependence of the point-spread function to classify the colors of single emitters imaged on a grayscale camera. While existing localization microscopy methods for spectral classification require additional optical elements in the emission path, e.g., spectral filters, prisms, or phase masks, our neural net correctly identifies static and mobile emitters with high efficiency using a standard, unmodified single-channel configuration. Furthermore, we show how deep learning can be used to design new phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species, including simultaneously differentiating four species in a single image. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keywords:
SINGLE-MOLECULE MICROSCOPY
SUPERRESOLUTION
RESOLUTION
DEPLETION
NANOSCOPY
EMISSION
TRACKING
LIMIT
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W