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@article{Boland:2021:10.1098/rsta.2020.0298,
author = {Boland, MA and Cohen, EAK and Flaxman, SR and Neil, MAA},
doi = {10.1098/rsta.2020.0298},
journal = {PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES},
title = {Improving axial resolution in Structured Illumination Microscopy using deep learning},
url = {http://dx.doi.org/10.1098/rsta.2020.0298},
volume = {379},
year = {2021}
}
TY - JOUR
AB - Structured Illumination Microscopy is a widespreadmethodology to image live and fixed biologicalstructures smaller than the diffraction limits ofconventional optical microscopy. Using recent advancesin image up-scaling through deep learning models,we demonstrate a method to reconstruct 3D SIMimage stacks with twice the axial resolution attainablethrough conventional SIM reconstructions. We furtherdemonstrate our method is robust to noise & evaluateit against two point cases and axial gratings. Finally,we discuss potential adaptions of the method tofurther improve resolution.
AU - Boland,MA
AU - Cohen,EAK
AU - Flaxman,SR
AU - Neil,MAA
DO - 10.1098/rsta.2020.0298
PY - 2021///
SN - 1364-503X
TI - Improving axial resolution in Structured Illumination Microscopy using deep learning
T2 - PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
UR - http://dx.doi.org/10.1098/rsta.2020.0298
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000644937400002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
VL - 379
ER -