From 34d0b9f85c3bc73416f1dc2bb25ef669b9ff8076 Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Tue, 15 Jan 2019 15:23:43 +0000 Subject: added jenkins build status --- Readme.md | 1 + 1 file changed, 1 insertion(+) (limited to 'Readme.md') diff --git a/Readme.md b/Readme.md index cdf823d..70c9e13 100644 --- a/Readme.md +++ b/Readme.md @@ -1,3 +1,4 @@ +[![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit)](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.** -- cgit v1.2.3