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author | Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk> | 2019-01-28 13:24:07 +0000 |
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committer | GitHub <noreply@github.com> | 2019-01-28 13:24:07 +0000 |
commit | d5ae6207e1045af233b9c27f94d78a3e1e8600bd (patch) | |
tree | fd9a560e080536b3afdab095bee5e962f3b41a49 | |
parent | 0d74c50c48ae518fedb44e5d04a148eaa02b485b (diff) | |
parent | 73eab10a44572b117054c3cff2cc1f14c58fdfa7 (diff) | |
download | regularization-d5ae6207e1045af233b9c27f94d78a3e1e8600bd.tar.gz regularization-d5ae6207e1045af233b9c27f94d78a3e1e8600bd.tar.bz2 regularization-d5ae6207e1045af233b9c27f94d78a3e1e8600bd.tar.xz regularization-d5ae6207e1045af233b9c27f94d78a3e1e8600bd.zip |
Merge pull request #94 from TomasKulhanek/master
Universal build script
-rw-r--r-- | Readme.md | 5 | ||||
-rwxr-xr-x | build/jenkins-build.sh | 2 |
2 files changed, 5 insertions, 2 deletions
@@ -1,7 +1,10 @@ +# CCPi-Regularisation Toolkit (CCPi-RGL) + + + | Master | Development | |--------|-------------| | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit-dev)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit-dev/) | -# CCPi-Regularisation Toolkit (CCPi-RGL) **Iterative image reconstruction (IIR) methods normally require regularisation to stabilise the convergence and make the reconstruction problem (inverse problem) more well-posed. The CCPi-RGL software provides 2D/3D and multi-channel regularisation strategies to ensure better performance of IIR methods. The regularisation modules are well-suited to use with [splitting algorithms](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method#Alternating_direction_method_of_multipliers), such as, [ADMM](https://github.com/dkazanc/ADMM-tomo) and [FISTA](https://github.com/dkazanc/FISTA-tomo). Furthermore, the toolkit can be used for simpler inversion tasks, such as, image denoising, inpaiting, deconvolution etc. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** diff --git a/build/jenkins-build.sh b/build/jenkins-build.sh index 190ac92..5b6c9ce 100755 --- a/build/jenkins-build.sh +++ b/build/jenkins-build.sh @@ -1,2 +1,2 @@ #!/usr/bin/env bash -bash <(curl -L https://raw.githubusercontent.com/TomasKulhanek/CCPi-VirtualMachine/master/scripts/jenkins-build.sh) +bash <(curl -L https://raw.githubusercontent.com/vais-ral/CCPi-VirtualMachine/master/scripts/jenkins-build.sh) |