# CCPi-Regularisation Toolkit (CCPi-RUT)

**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise convergence and make the reconstruction problem more well-posed. 
CCPi-RUT is released under Apache 2.0 license and consists of 2D/3D regularisation methods 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/C++ compilers
 * nvcc compilers

## Package Contents :

  * 1. Rudin-Osher-Fatemi Total Variation (explicit PDE minimisation scheme) 2D/3D GPU/CPU [1]
  * 2. Fast-Gradient-Projection Total Variation 2D/3D GPU/CPU [2]

### Demos:
 * ---

### Installation:

#### Python (conda-build)
```
     export CIL_VERSION=0.9.2
```
#### 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.

### Acknowledgment:
CCPi-RUT is a product of the [CCPi project](https://pages.github.com/)
any questions/comments please e-mail to daniil.kazantsev@manchester.ac.uk