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ToMoBAR is a library of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability
A wrapper around ASTRA-toolbox to simplify access to various reconstruction methods ASTRA has
Regularised iterative ordered-subsets FISTA reconstruction algorithm with linear and non-linear data fidelities
Regularised iterative ADMM reconstruction algorithm
Demos to reconstruct synthetic and also real data (provided) [4-6]
- Tomographic parallel-beam projection data can be simulated without the "inverse crime" using TomoPhantom. Noise and artifacts (zingers, rings, jitter) can be modelled and added to the data.
- Simulated data reconstructed iteratively using FISTA or ADMM algorithms with multiple "plug-and-play" regularisers from CCPi-RegularisationToolkit.
- The FISTA algorithm offers various modifications: convergence acceleration with ordered-subsets method, PWLS, Huber, Group-Huber[3] and Students't data fidelities [1,2] to deal with noise and imaging artifacts (rings, streaks).
- MATLAB or Python
- ASTRA-toolbox for projection operations
- TomoPhantom for simulation
- CCPi-RegularisationToolkit for regularisation
- See INSTALLATION for detailed information
For building on Linux see run.sh
Install from the conda channel:
conda install -c dkazanc tomobar
or build with:
export VERSION=`date +%Y.%m` (unix) / set VERSION=2019.11 (Windows)
conda build conda-recipe/ --numpy 1.16 --python 3.6
conda install tomobar --use-local --force-reinstall # if this don't work (probably you're on Python 3*)
conda install -c file://${CONDA_PREFIX}/conda-bld/ tomobar --force-reinstall # try this one
Simply use available m-functions, see Demos
- D. Kazantsev et al. 2017. A Novel Tomographic Reconstruction Method Based on the Robust Student's t Function For Suppressing Data Outliers. IEEE TCI, 3(4), pp.682-693.
- D. Kazantsev et al. 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9), p.094004.
- P. Paleo and A. Mirone, 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278.
- D. Kazantsev et al. 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.
- E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.
- E. Guo et al. 2018. Revealing the microstructural stability of a three-phase soft solid (ice cream) by 4D synchrotron X-ray tomography. Journal of Food Engineering
- E. Guo et al. 2017. Dendritic evolution during coarsening of Mg-Zn alloys via 4D synchrotron tomography. Acta Materialia
- E. Guo et al. 2017. Synchrotron X-ray tomographic quantification of microstructural evolution in ice cream–a multi-phase soft solid. Rsc Advances
GNU GENERAL PUBLIC LICENSE v.3
can be addressed to Daniil Kazantsev at dkazanc@hotmail.com