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| author | vais-ral <rlcr8@stfc.ac.uk> | 2019-02-12 09:43:26 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-02-12 09:43:26 +0000 |
| commit | bc475e38fef30aa19bdfe6f04e81c30cd3c9c9b5 (patch) | |
| tree | 0d1c953c76b058838b80d91cae4ee0744dea2add | |
| parent | 7f1612a9bff83959dcb7d62dd404b6dccf6f08a3 (diff) | |
| download | regularization-bc475e38fef30aa19bdfe6f04e81c30cd3c9c9b5.tar.gz regularization-bc475e38fef30aa19bdfe6f04e81c30cd3c9c9b5.tar.bz2 regularization-bc475e38fef30aa19bdfe6f04e81c30cd3c9c9b5.tar.xz regularization-bc475e38fef30aa19bdfe6f04e81c30cd3c9c9b5.zip | |
added conda badges
| -rw-r--r-- | Readme.md | 6 |
1 files changed, 3 insertions, 3 deletions
@@ -2,9 +2,9 @@ -| Master | Development | -|--------|-------------| -| [](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/) | +| Master | Development | Anaconda binaries | +|--------|-------------|-------------------| +| [](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 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.** |