Resolution enhancement in scanning electron microscopy using deep learning

Abstract We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the...

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Autores principales:	Kevin de Haan, Zachary S. Ballard, Yair Rivenson, Yichen Wu, Aydogan Ozcan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/67a6f65d728340228b53d78e8e54f33f
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spelling	oai:doaj.org-article:67a6f65d728340228b53d78e8e54f33f2021-12-02T15:08:46ZResolution enhancement in scanning electron microscopy using deep learning10.1038/s41598-019-48444-22045-2322https://doaj.org/article/67a6f65d728340228b53d78e8e54f33f2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-48444-2https://doaj.org/toc/2045-2322Abstract We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.Kevin de HaanZachary S. BallardYair RivensonYichen WuAydogan OzcanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-7 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kevin de Haan Zachary S. Ballard Yair Rivenson Yichen Wu Aydogan Ozcan Resolution enhancement in scanning electron microscopy using deep learning
description	Abstract We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-resolution technique by inferring unresolved features in low-resolution SEM images and comparing them with the accurately co-registered high-resolution SEM images of the same samples. Through spatial frequency analysis, we also report that our method generates images with frequency spectra matching higher resolution SEM images of the same fields-of-view. By using this technique, higher resolution SEM images can be taken faster, while also reducing both electron charging and damage to the samples.
format	article
author	Kevin de Haan Zachary S. Ballard Yair Rivenson Yichen Wu Aydogan Ozcan
author_facet	Kevin de Haan Zachary S. Ballard Yair Rivenson Yichen Wu Aydogan Ozcan
author_sort	Kevin de Haan
title	Resolution enhancement in scanning electron microscopy using deep learning
title_short	Resolution enhancement in scanning electron microscopy using deep learning
title_full	Resolution enhancement in scanning electron microscopy using deep learning
title_fullStr	Resolution enhancement in scanning electron microscopy using deep learning
title_full_unstemmed	Resolution enhancement in scanning electron microscopy using deep learning
title_sort	resolution enhancement in scanning electron microscopy using deep learning
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/67a6f65d728340228b53d78e8e54f33f
work_keys_str_mv	AT kevindehaan resolutionenhancementinscanningelectronmicroscopyusingdeeplearning AT zacharysballard resolutionenhancementinscanningelectronmicroscopyusingdeeplearning AT yairrivenson resolutionenhancementinscanningelectronmicroscopyusingdeeplearning AT yichenwu resolutionenhancementinscanningelectronmicroscopyusingdeeplearning AT aydoganozcan resolutionenhancementinscanningelectronmicroscopyusingdeeplearning
_version_	1718388003087319040

Resolution enhancement in scanning electron microscopy using deep learning

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