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author | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-12-06 12:39:13 +0000 |
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committer | GitHub <noreply@github.com> | 2018-12-06 12:39:13 +0000 |
commit | 3bce1f1410303a6833d1e647fba9692ea40fa878 (patch) | |
tree | b5c970d19e4cf657f013349a81cd7bf30caa44df | |
parent | 9fa6e4fedc4685356467e1d685601e47e6176c9f (diff) | |
download | regularization-3bce1f1410303a6833d1e647fba9692ea40fa878.tar.gz regularization-3bce1f1410303a6833d1e647fba9692ea40fa878.tar.bz2 regularization-3bce1f1410303a6833d1e647fba9692ea40fa878.tar.xz regularization-3bce1f1410303a6833d1e647fba9692ea40fa878.zip |
readme update3
-rw-r--r-- | Readme.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -1,6 +1,6 @@ # CCPi-Regularisation Toolkit (CCPi-RGL) -**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise the convergence and make the reconstruction problem more well-posed. CCPi-RGL software consists of 2D/3D regularisation modules for single-channel and multi-channel reconstruction problems. 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 and FISTA. Furthermore, the toolkit can be used independently to solve image denoising and inpaiting tasks. The core modules are written in C-OMP and CUDA languages, wrappers for Matlab and Python are provided.** +**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> @@ -162,7 +162,7 @@ addpath(/path/to/library); ### Applications: -* [Regularised FISTA iterative reconstruction algorithm for X-ray tomographic reconstruction with highly inaccurate measurements (MATLAB code)](https://github.com/dkazanc/FISTA-tomo) +* [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) * [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) |