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CLC number: O436

On-line Access: 2017-10-25

Received: 2016-10-14

Revision Accepted: 2016-12-13

Crosschecked: 2017-09-25

Cited: 1

Clicked: 1609

Citations:  Bibtex RefMan EndNote GB/T7714


Zhi-ping Zeng


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1222-1235


Computational methods in super-resolution microscopy

Author(s):  Zhi-ping Zeng, Hao Xie, Long Chen, Karl Zhanghao, Kun Zhao, Xu-san Yang, Peng Xi

Affiliation(s):  College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China; more

Corresponding email(s):   zhipingzeng@fzu.edu.cn, xipeng@pku.edu.cn

Key Words:  Super-resolution microscopy, Deconvolution, Computational methods

Zhi-ping Zeng, Hao Xie, Long Chen, Karl Zhanghao, Kun Zhao, Xu-san Yang, Peng Xi. Computational methods in super-resolution microscopy[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1222-1235.

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T1 - Computational methods in super-resolution microscopy
A1 - Zhi-ping Zeng
A1 - Hao Xie
A1 - Long Chen
A1 - Karl Zhanghao
A1 - Kun Zhao
A1 - Xu-san Yang
A1 - Peng Xi
J0 - Frontiers of Information Technology & Electronic Engineering
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SP - 1222
EP - 1235
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601628

The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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