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# CCPi-Regularisation Toolkit (CCPi-RGL)
**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise convergence and make the reconstruction problem more well-posed.
CCPi-RGL software consist of 2D/3D regularisation modules which frequently used for IIR.
The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.**
## Prerequisites:
* MATLAB (www.mathworks.com/products/matlab/)
* Python (ver. 3.5); Cython
* C compilers
* nvcc (CUDA SDK) compilers
## Package modules (regularisers):
1. Rudin-Osher-Fatemi Total Variation (explicit PDE minimisation scheme) [2D/3D GPU/CPU]
2. Fast-Gradient-Projection Total Variation [2D/3D GPU/CPU]
### Installation:
#### Python (conda-build)
```
export CIL_VERSION=0.9.2
conda build recipes/regularizers --numpy 1.12 --python 3.5
conda install cil_regularizer=0.9.2 --use-local --force
cd Wrappers/Python
conda build conda-recipe --numpy 1.12 --python 3.5
conda install ccpi-regularizer=0.9.2 --use-local --force
cd test/
python test_cpu_vs_gpu_regularizers.py
```
#### Matlab
### References:
1. Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.
2. Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434.
3. Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
### License:
[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)
### Acknowledgments:
CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group and STFC SCD software developers. Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
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