diff options
| author | Tomas Kulhanek <tomas.kulhanek@stfc.ac.uk> | 2019-01-21 17:16:52 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-01-21 17:16:52 +0000 |
| commit | 39dc8ebc5e4bbb3b4138156d21d2e970ece2fbc5 (patch) | |
| tree | 8b59253d9e35ee5eab0b165ca7cc0126be8b6b43 /Readme.md | |
| parent | b9ee6fad5b3119e738f6999eb4dd78bafb98a7cd (diff) | |
| parent | 60b4d51ce1cf19be2a3483232448ae227253614c (diff) | |
| download | regularization-39dc8ebc5e4bbb3b4138156d21d2e970ece2fbc5.tar.gz regularization-39dc8ebc5e4bbb3b4138156d21d2e970ece2fbc5.tar.bz2 regularization-39dc8ebc5e4bbb3b4138156d21d2e970ece2fbc5.tar.xz regularization-39dc8ebc5e4bbb3b4138156d21d2e970ece2fbc5.zip | |
Merge pull request #1 from TomasKulhanek/pr-test
Pr test
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 1 |
1 files changed, 1 insertions, 0 deletions
@@ -1,3 +1,4 @@ +[](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) # 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.** |