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| author | Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk> | 2019-01-23 11:02:32 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-01-23 11:02:32 +0000 |
| commit | 6c86733638b566fe5572fd0965def50d425502ab (patch) | |
| tree | eef6d2ac6d92f2aa3c1d6de388c1a230e0959b83 | |
| parent | 619e972559992684854eef854e60fbc363e93819 (diff) | |
| download | regularization-6c86733638b566fe5572fd0965def50d425502ab.tar.gz regularization-6c86733638b566fe5572fd0965def50d425502ab.tar.bz2 regularization-6c86733638b566fe5572fd0965def50d425502ab.tar.xz regularization-6c86733638b566fe5572fd0965def50d425502ab.zip | |
Update Readme.md
| -rw-r--r-- | Readme.md | 3 |
1 files changed, 2 insertions, 1 deletions
@@ -1,7 +1,8 @@ +# CCPi-Regularisation Toolkit (CCPi-RGL) + | 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.** |