Computational methods in super-resolution microscopy

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.

Keywords

Super-resolution microscopy / Deconvolution / Computational methods

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