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| author | Edoardo Pasca <edo.paskino@gmail.com> | 2019-01-15 15:23:43 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-01-15 15:23:43 +0000 |
| commit | 34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076 (patch) | |
| tree | 314ae9ddaaad207a67226961d6302b5817764a16 /Readme.md | |
| parent | 3b114face28547468da9e1b491c67bd5ea53603b (diff) | |
| download | regularization-34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076.tar.gz regularization-34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076.tar.bz2 regularization-34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076.tar.xz regularization-34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076.zip | |
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@@ -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.** |