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Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 12 |
1 files changed, 8 insertions, 4 deletions
@@ -27,6 +27,7 @@ 5. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *8*) 6. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *9*) 7. A joint ROF-LLT (Lysaker-Lundervold-Tai) model for higher-order regularisation **2D/3D CPU/GPU** (Ref. *10,11*) +8. Nonlocal Total Variation regularisation (GS fixed point iteration) **2D/3D CPU/GPU** (Ref. *12*) ### Multi-channel (denoising): 1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,4,2*) @@ -60,18 +61,19 @@ Flags used during configuration | `Matlab_ROOT_DIR` | path | Matlab directory| |`PYTHON_EXECUTABLE` | path | /path/to/python/executable| -Here an example of build on Linux: +Here an example of build on Linux (see also `run.sh` for additional info): ```bash git clone https://github.com/vais-ral/CCPi-Regularisation-Toolkit.git mkdir build cd build -cmake ../CCPi-Regularisation-Toolkit -DCONDA_BUILD=OFF -DBUILD_MATLAB_WRAPPER=ON -DBUILD_PYTHON_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=<your favourite install directory> +cmake .. -DCONDA_BUILD=OFF -DBUILD_MATLAB_WRAPPER=ON -DBUILD_PYTHON_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install make install +cd install/python +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib ``` - ### Python #### Python binaries Python binaries are distributed via the [ccpi](https://anaconda.org/ccpi/ccpi-regulariser) conda channel. Currently we produce packages for Linux64, Python 2.7, 3.5 and 3.6, NumPy 1.12 and 1.13. @@ -82,7 +84,7 @@ conda install ccpi-regulariser -c ccpi -c conda-forge #### Python (conda-build) ``` - export CIL_VERSION=0.10.1 + export CIL_VERSION=0.10.2 conda build Wrappers/Python/conda-recipe --numpy 1.12 --python 3.5 conda install ccpi-regulariser=${CIL_VERSION} --use-local --force cd demos/ @@ -149,6 +151,8 @@ addpath(/path/to/library); 11. [Kazantsev, D., Guo, E., Phillion, A.B., Withers, P.J. and Lee, P.D., 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9), p.094004.](https://doi.org/10.1088/1361-6501/aa7fa8) +12. [Abderrahim E., Lezoray O. and Bougleux S. 2008. Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans. Image Processing 17(7), pp. 1047-1060.](https://ieeexplore.ieee.org/document/4526700) + ### References to Software: * If software is used, please refer to [11], however, the supporting publication is in progress. |