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@@ -1,24 +1,27 @@ -# CCPi-Regularisation Toolkit (CCPi-RGL) +# CCPi-Regularization 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.** +**Iterative image reconstruction (IIR) methods normally require regularization to stabilize the convergence and make the reconstruction problem more well-posed. +CCPi-RGL software consist of 2D/3D regularization modules for single-channel and multi-channel reconstruction problems. The modules especially suited for IIR, however, +can also be used as image denoising iterative filters. 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 + * MATLAB (www.mathworks.com/products/matlab/) OR + * Python (tested 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] +### Single-channel +1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) [2D/3D GPU/CPU]; (Ref. 1) +2. Fast-Gradient-Projection (FGP) Total Variation [2D/3D GPU/CPU]; (Ref. 2) -### Installation: +### Multi-channel -#### Python (conda-build) +## Installation: + +### Python (conda-build) ``` export CIL_VERSION=0.9.2 conda build recipes/regularizers --numpy 1.12 --python 3.5 @@ -29,7 +32,12 @@ The core modules are written in C-OMP and CUDA languages and wrappers for Matlab cd test/ python test_cpu_vs_gpu_regularizers.py ``` -#### Matlab +### Matlab +``` + cd /Wrappers/Matlab/mex_compile + compileCPU_mex.m % to compile CPU modules + compileGPU_mex.m % to compile GPU modules (see instructions in the file) +``` ### 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. |