Samira Kabri1, Alexander Auras2, Danilo Riccio3, Martin Benning3,4, Michael Moeller2 and Martin Burger1
1Department of Mathematics, University of Erlangen-Nuremberg2Institute for Vision and Graphics, University of Siegen
3School of Mathematical Sciences, Queen Mary University of London
4The Alan Turing Institute, British Library
This repository contains the official pytorch implementation of Convergent Data-driven Regularizations for CT Reconstruction.
- Calculation of
$\Pi$ ,$\Delta$ and$\Gamma$ - Calculation of the analytic coefficients
$g_n$ - Learning of the coefficients
$\overline{g_n}$ - Calculation of the analytic filter
$\rho$ - Learning of the filter
$\overline{\rho}$ - Logging of results via tensorboard
- Saving of results in .pt files
Dependencies to execute the calculations and the used versions:
- torch (1.12.1)
- hydra-core (1.2.0)
- pytorch-lightning (1.7.1)
- torchmetrics (0.9.3)
- radon (1.0.0) (either as submodule or from the repository at https://github.com/AlexanderAuras/radon)
Optional dependencies for the visualizations:
- notebook
- matplotlib
- tensorflow
Dependencies to view tensorboard results:
- tensorboard
- Install conda and create environment (optional)
- Install dependencies
- Clone repository:
git clone https://github.com/AlexanderAuras/LearnedRadonFilters
- Adjust the output directory in configs/default.yaml and the paths in mnist_datamodule.py
- Adjust the configurations in the configs folder
- Adjust configurations for datasets in configs/dataset/<dataset>.yaml
- Adjust configurations for the models in configs/model/<model>.yaml
- Adjust general configurations in configs/default.yaml
- Execute the training/calculations with
python main.py hydra.job.name=<name>
- View tensorboard results via
tensorboard --logdir=<output_directory>
- View saved .pt files in the jupyter notebooks plots.ipynb, test.ipynb or visualization.ipynb
- For every run
$\Pi$ ,$\Delta$ and$\Gamma$ are saved in pi.pt, delta.pt and gamma.pt - For FFT-runs, coefficients.pt contains
$\rho$ or$\overline{\rho}$ - For SVD-runs, coefficients.pt contains
$g_n$ or$\overline{g_n}$ . - For SVD-runs the singular values are saved in singular_values.pt
- For every run