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This repository contains the jupyter notebooks, python libraries, and information needed to reproduce the results and figures in the paper "Noise-resilient deep tomographic imaging" (https://preprints.opticaopen.org/articles/preprint/Noise-resilient_deep_tomographic_imaging/21931557).

The git repository contains all the code needed, however there is also a substantial amount of data associated with this work which cannot be distributed via a git repository. The data can be found by request to the author.

• The FBP_algo folder contains all the important functions to compute FBP reconstruction.
• The util folder contains all the important functions to train our neural network.
• other .py files are used to generate iterative reconstructions and train the neural networks.
• Simulate_Projections.ipynb is used to generate simulated data the X-ray projections for the paper's simulation section.
• Sim_Results.ipynb is used to perform analysis and generate figures for the simulation section of the paper.
• Experimental_Results.ipynb is used to perform analysis and generate figures for the experimental section of the paper.