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2018 ; 8
(1
): 5700
Nephropedia Template TP
Sci Rep
2018[Apr]; 8
(1
): 5700
PMID29686251
show ga
Capturing biological dynamics with high spatiotemporal resolution demands the
advancement in imaging technologies. Super-resolution fluorescence microscopy
offers spatial resolution surpassing the diffraction limit to resolve
near-molecular-level details. While various strategies have been reported to
improve the temporal resolution of super-resolution imaging, all super-resolution
techniques are still fundamentally limited by the trade-off associated with the
longer image acquisition time that is needed to achieve higher spatial
information. Here, we demonstrated an example-based, computational method that
aims to obtain super-resolution images using conventional imaging without
increasing the imaging time. With a low-resolution image input, the method
provides an estimate of its super-resolution image based on an example database
that contains super- and low-resolution image pairs of biological structures of
interest. The computational imaging of cellular microtubules agrees approximately
with the experimental super-resolution STORM results. This new approach may offer
potential improvements in temporal resolution for experimental super-resolution
fluorescence microscopy and provide a new path for large-data aided biomedical
imaging.