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| -rw-r--r-- | Readme.md | 9 | ||||
| -rw-r--r-- | demos/images/CCPiRGL_sm.jpg | bin | 0 -> 220809 bytes |
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@@ -4,19 +4,14 @@ |--------|-------------|-------------------| | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/) | [](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit-dev/) |   [ ](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/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.** <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/TV_vs_NLTV.jpg" height="300"><br> + <img src="demos/images/CCPiRGL_sm.jpg" height="400"><br> </div> ## Prerequisites: diff --git a/demos/images/CCPiRGL_sm.jpg b/demos/images/CCPiRGL_sm.jpg Binary files differnew file mode 100644 index 0000000..c65cc1d --- /dev/null +++ b/demos/images/CCPiRGL_sm.jpg |
