Papers › Improving axial resolution in SIM using deep learning
Improving axial resolution in SIM using deep learning
Miguel Boland, Edward A. K. Cohen, Seth Flaxman, Mark A. A. Neil
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.
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