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Super-resolution microscopy using deep learning (Conference Presentation)
Author(s): Aydogan Ozcan
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Paper Abstract

Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. The name comes from the general structure of deep neural networks, which consist of several layers of artificial neurons, each performing a nonlinear operation, stacked over each other. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, reconstruction and sensing fields. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks for achieving super-resolution in optical microscopy across different imaging modalities.

Paper Details

Date Published: 13 March 2019
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Proc. SPIE 10884, Single Molecule Spectroscopy and Superresolution Imaging XII, 108840S (13 March 2019); doi: 10.1117/12.2509120
Show Author Affiliations
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10884:
Single Molecule Spectroscopy and Superresolution Imaging XII
Zygmunt Karol Gryczynski; Ingo Gregor; Felix Koberling, Editor(s)

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