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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2019-06-02 14:07:14 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-06-02 14:07:14 +0100 |
| commit | 57229985e3576db6a714d7e4a35aec4f47120e1b (patch) | |
| tree | 8716aa2a793e525692e2516133d438e97c124f4f /Readme.md | |
| parent | 5fb5be32360bab1b6619bcef192dde0878d014ff (diff) | |
| parent | a532c5efb8f20a8eb400fdad0edc00dad50a5c46 (diff) | |
| download | regularization-57229985e3576db6a714d7e4a35aec4f47120e1b.tar.gz regularization-57229985e3576db6a714d7e4a35aec4f47120e1b.tar.bz2 regularization-57229985e3576db6a714d7e4a35aec4f47120e1b.tar.xz regularization-57229985e3576db6a714d7e4a35aec4f47120e1b.zip | |
Merge pull request #124 from vais-ral/tests_nopillow
Dropping pillow dependency fot unittests
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 8 |
1 files changed, 4 insertions, 4 deletions
@@ -4,7 +4,7 @@ |--------|-------------|-------------------| | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit-dev/) |   [ ](https://anaconda.org/ccpi/ccpi-regulariser) | -**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (CCPi-RGL) toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** +**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (CCPi-RGL) toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** <div align="center"> @@ -100,9 +100,9 @@ conda install ccpi-regulariser -c ccpi -c conda-forge #### Python (conda-build) ``` - export CIL_VERSION=19.03 - conda build recipe/ --numpy 1.12 --python 3.5 - conda install ccpi-regulariser=${CIL_VERSION} --use-local --force + export CIL_VERSION=19.06 (Unix) / set CIL_VERSION=19.06 (Windows) + conda build recipe/ --numpy 1.15 --python 3.7 + conda install ccpi-regulariser=${CIL_VERSION} --use-local --force-reinstall cd demos/ python demo_cpu_regularisers.py # to run CPU demo python demo_gpu_regularisers.py # to run GPU demo |