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| author | Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk> | 2019-01-23 10:08:11 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-01-23 10:08:11 +0000 |
| commit | 00bcc569758d9429ecb3234e64c7ac05b3f8e3c1 (patch) | |
| tree | bb98716fc681b88e65e994a5902acf931952444e /Readme.md | |
| parent | 7c9c66198cba8e1b83d0e1c57a150aae4e3644f6 (diff) | |
| download | regularization-00bcc569758d9429ecb3234e64c7ac05b3f8e3c1.tar.gz regularization-00bcc569758d9429ecb3234e64c7ac05b3f8e3c1.tar.bz2 regularization-00bcc569758d9429ecb3234e64c7ac05b3f8e3c1.tar.xz regularization-00bcc569758d9429ecb3234e64c7ac05b3f8e3c1.zip | |
Update Readme.md
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| -rw-r--r-- | Readme.md | 3 |
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@@ -1,4 +1,5 @@ -[](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) +Master: [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) +Development status (PR, non-master branch): [](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.** |