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.
Highlights
The past few decades have testified to the rapid development of various super-resolution microscopy techniques (Ding et al, 2011; Gu et al, 2014; Gao et al, 2016; Yang et al, 2016a; Yu et al, 2016)
Because the process of optical imaging is the convolution of the original object and the point spread function (PSF) of the system, the wave nature of light limits the resolution of conventional optical microscopy
The advantage of blind deconvolution is that the spatial distribution of the PSF of the system is not required, which is difficult or even impossible to obtain in some situations
Summary
The past few decades have testified to the rapid development of various super-resolution microscopy techniques (Ding et al, 2011; Gu et al, 2014; Gao et al, 2016; Yang et al, 2016a; Yu et al, 2016). Because the process of optical imaging is the convolution of the original object and the point spread function (PSF) of the system, the wave nature of light limits the resolution of conventional optical microscopy. Super-resolution optical fluctuation imaging (SOFI) employs correlation analysis on the temporal fluctuation of the fluorescence to distinguish signals from independently fluctuating fluorophores (Dertinger et al, 2009). The achievable resolution of SMLM, e.g., PALM/STORM, can be down to 10–20 nm. Computational methods in super-resolution microscopy are extremely important for achieving high-quality super-resolution imaging
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
