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Improving axial resolution in Structured Illumination Microscopy using deep learning
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DOI：10.1098/rsta.2020.0298.png)
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Structured Illumination Microscopy (SIM) 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 demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.
Keyword:
microscopy
deep learning
structure illumination
residual channel attention network
Structured Illumination Microscopy
期刊
P
IF:
3.7
论文数:
7.8K
被引数:
2.8W
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引用论文
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