softwareX demos updated

1 files changed, 12 insertions, 3 deletions

diff --git a/Readme.md b/Readme.md
index 6c45023..f736415 100644
--- a/Readme.md
+++ b/Readme.md
@@ -4,10 +4,19 @@
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 | [![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/) | [![Build Status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?job=CILsingle/CCPi-Regularisation-Toolkit-dev)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit-dev/) | ![conda version](https://anaconda.org/ccpi/ccpi-regulariser/badges/version.svg) ![conda last release](https://anaconda.org/ccpi/ccpi-regulariser/badges/latest_release_date.svg) [![conda platforms](https://anaconda.org/ccpi/ccpi-regulariser/badges/platforms.svg) ![conda dowloads](https://anaconda.org/ccpi/ccpi-regulariser/badges/downloads.svg)](https://anaconda.org/ccpi/ccpi-regulariser) |
 
-**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-RGL toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/TomoRec), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.**
+**Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-RGL toolkit provides a set of 2D/3D regularisation strategies to guarantee a better performance of IIR methods (higher SNR and resolution). The regularisation modules for scalar and vectorial datasets are based on the [proximal operator](https://en.wikipedia.org/wiki/Proximal_operator) framework and can be used with [proximal splitting algorithms](https://en.wikipedia.org/wiki/Proximal_gradient_method), such as PDHG, Douglas-Rachford, ADMM, FISTA and [others](https://arxiv.org/abs/0912.3522). While the main target for CCPi-RGL is [tomographic image reconstruction](https://github.com/dkazanc/ToMoBAR), the toolkit can be used for image denoising and inpaiting problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** 
+
+
+<div align="center">
+  <img src="demos/images/probl.png" height="225"><br>  
+</div>
+
+<div align="center">
+  <img src="demos/images/reg_penalties.jpg" height="450"><br>  
+</div>
 
 <div align="center">
-  <img src="demos/images/CCPiRGL_sm.jpg" height="400"><br>  
+  <img src="demos/images/TV_vs_NLTV.jpg" height="300"><br>  
 </div>
 
 ## Prerequisites:
@@ -173,7 +182,7 @@ addpath(/path/to/library);
 
 ### Applications:
 
-* [A library of tomographic reconstruction methods: direct and model-based iterative (MATLAB/Python code)](https://github.com/dkazanc/TomoRec)
+* [TOmographic MOdel-BAsed Reconstruction (ToMoBAR)](https://github.com/dkazanc/ToMoBAR)
 * [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: