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Improving axial resolution in SIM using deep learning

Miguel Boland 1, Dr. Edward A.K. Cohen 1, Dr. Seth Flaxman1 & Professor Mark Neil 2

1Imperial College London, Faculty of Natural Sciences, Department of Mathematics 2Imperial College London, Faculty of Natural Sciences, Department of Physics

Imperial College London

Pre-print available:

https://arxiv.org/pdf/2009.02264v3.pdf

Repository organisation

The code in this repository is organised as follows:

Image simulation

Contains all code required to generate the training data used in our experiment, in both raw SIM image stacks (inputs) and a high resolution confocal output image (target)

Deep learning

Contains all code required to generate training data from existing TIFF files, and produce a trained RCAN model on this data.

Image evaluation

Contains code required to run the RCAN models on a set of test images, and generate comparison metrics with SIM reconstructions and simulated high-resolution target data.

Please feel free to ask any questions using Github Issues!

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