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| author | Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk> | 2019-01-23 11:26:10 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-01-23 11:26:10 +0000 |
| commit | b38ac34e763aa442a19dff7c2c22f8892cc0cd3c (patch) | |
| tree | b8f3f1235b174a9bb2613eb5e54099f3a1860847 /Readme.md | |
| parent | 9f186d5978aa4e528ea071d5de0a74edb71916ad (diff) | |
| parent | 619e972559992684854eef854e60fbc363e93819 (diff) | |
| download | regularization-b38ac34e763aa442a19dff7c2c22f8892cc0cd3c.tar.gz regularization-b38ac34e763aa442a19dff7c2c22f8892cc0cd3c.tar.bz2 regularization-b38ac34e763aa442a19dff7c2c22f8892cc0cd3c.tar.xz regularization-b38ac34e763aa442a19dff7c2c22f8892cc0cd3c.zip | |
Merge pull request #92 from TomasKulhanek/master
build scripts detects tag and branch
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| -rw-r--r-- | Readme.md | 4 |
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@@ -1,4 +1,6 @@ -[](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) +| 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/) | # 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.** |