Computational methods in super-resolution microscopy

被引：0
作者：
Zhi-ping Zeng
Hao Xie
Long Chen
Karl Zhanghao
Kun Zhao
Xu-san Yang
Peng Xi
机构：
[1] Fuzhou University,College of Physics and Information Engineering
[2] Peking University,Department of Biomedical Engineering
[3] Tsinghua University,MOE Key Laboratory of Bioinformatics
[4] Tsinghua University,Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST
[5] Tsinghua University,Department of Automation
来源：
Frontiers of Information Technology & Electronic Engineering | 2017年 / 18卷
关键词：
Super-resolution microscopy; Deconvolution; Computational methods; O436;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1222 / 1235
页数：13
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