Improving axial resolution in SIM using deep learning
Image and Video Processing
2021-02-19 v3 Computer Vision and Pattern Recognition
Biological Physics
Abstract
Structured Illumination Microscopy is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further evaluate our method for robustness to noise & generalisability to varying observed specimens, and discuss potential adaptions of the method to further improvements in resolution.
Cite
@article{arxiv.2009.02264,
title = {Improving axial resolution in SIM using deep learning},
author = {Miguel Boland and Edward A. K. Cohen and Seth Flaxman and Mark A. A. Neil},
journal= {arXiv preprint arXiv:2009.02264},
year = {2021}
}