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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2019-02-22 12:41:28 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-02-22 12:41:28 +0000 |
| commit | c237d292999c93df09ca3679876d225896dd0ff9 (patch) | |
| tree | cf24793f6d269efbeeb10e96093b619be2a0f13d /Readme.md | |
| parent | 4505a79103e98adb33bfb4c10391319e56ae7031 (diff) | |
| download | regularization-c237d292999c93df09ca3679876d225896dd0ff9.tar.gz regularization-c237d292999c93df09ca3679876d225896dd0ff9.tar.bz2 regularization-c237d292999c93df09ca3679876d225896dd0ff9.tar.xz regularization-c237d292999c93df09ca3679876d225896dd0ff9.zip | |
updated readme
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 9 |
1 files changed, 4 insertions, 5 deletions
@@ -9,15 +9,15 @@ **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.** <div align="center"> - <img src="docs/images/probl.png" height="225"><br> + <img src="demos/images/probl.png" height="225"><br> </div> <div align="center"> - <img src="docs/images/reg_penalties.jpg" height="450"><br> + <img src="demos/images/reg_penalties.jpg" height="450"><br> </div> <div align="center"> - <img src="docs/images/TV_vs_NLTV.jpg" height="300"><br> + <img src="demos/images/TV_vs_NLTV.jpg" height="300"><br> </div> ## Prerequisites: @@ -183,8 +183,7 @@ addpath(/path/to/library); ### Applications: -* [Regularised FISTA iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements (MATLAB/Python code)](https://github.com/dkazanc/FISTA-tomo) -* [Regularised ADMM iterative reconstruction algorithm for X-ray tomographic reconstruction (MATLAB code)](https://github.com/dkazanc/ADMM-tomo) +* [A library of tomographic reconstruction methods: direct and model-based iterative (MATLAB/Python code)](https://github.com/dkazanc/TomoRec) * [Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography (MATLAB code)](https://github.com/dkazanc/multi-channel-X-ray-CT) ### License: |