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Frontiers of Information Technology & Electronic Engineering >> 2017, Volume 18, Issue 9 doi: 10.1631/FITEE.1601628

Computational methods in super-resolution microscopy

. College of Physics and InformationEngineering, Fuzhou University, Fuzhou 350116, China.. Department of Biomedical Engineering,Peking University, Beijing 100871, China.. MOE Key Laboratory of Bioinformatics,Tsinghua University, Beijing 100084, China.. Bioinformatics Division and Centerfor Synthetic & Systems Biology, TNLIST, Tsinghua University,Beijing 100084, China.. Department of Automation, TsinghuaUniversity, Beijing 100084, China

Available online: 2018-01-18

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Abstract

The broad applicability of super-resolution microscopy has beenwidely demonstrated in various areas and disciplines. The optimizationand improvement of algorithms used in super-resolution microscopyare of great importance for achieving optimal quality of super-resolutionimaging. In this review, we comprehensively discuss the computationalmethods in different types of super-resolution microscopy, includingdeconvolution microscopy, polarization-based super-resolution microscopy,structured illumination microscopy, image scanning microscopy, super-resolutionoptical fluctuation imaging microscopy, single-molecule localizationmicroscopy, Bayesian super-resolution microscopy, stimulated emissiondepletion microscopy, and translation microscopy. The developmentof novel computational methods would greatly benefit super-resolutionmicroscopy and lead to better resolution, improved accuracy, and fasterimage processing.

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