Computational methods in super-resolution microscopy

Review

Abstract

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.

Key words

Super-resolution microscopy Deconvolution Computational methods 

CLC number

O436 

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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of Physics and Information EngineeringFuzhou UniversityFuzhouChina
  2. 2.Department of Biomedical EngineeringPeking UniversityBeijingChina
  3. 3.MOE Key Laboratory of BioinformaticsTsinghua UniversityBeijingChina
  4. 4.Bioinformatics Division and Center for Synthetic & Systems Biology, TNLISTTsinghua UniversityBeijingChina
  5. 5.Department of AutomationTsinghua UniversityBeijingChina

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