Merge pull request #202 from TomographicImaging/post-migrate-tidy

docs: update links

Author: casperdcl

Installation.md

@@ -5,7 +5,7 @@
 In order to compile C/C++ sources and additional wrappers from source code for numpy 1.24 and python 3.10, the recommended way is:
 
 ```sh
-git clone https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 cd CCPi-Regularisation-Toolkit
 export CCPI_BUILD_ARGS="--numpy 1.24 --python 3.10"
 build/jenkins-build.sh
@@ -36,7 +36,7 @@ Flags used during configuration
 Here an example of build on Linux (see also `run.sh` for additional info):
 
 ```sh
-git clone https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+git clone https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 cd CCPi-Regularisation-Toolkit
 cmake -S . -B ./build_proj -DBUILD_MATLAB_WRAPPER=ON -DBUILD_PYTHON_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_INSTALL_PREFIX=./install
 cmake --build ./build_proj --target install
@@ -69,7 +69,7 @@ python demo_gpu_regularisers.py # to run GPU demo
 One can also use some of the GPU modules directly (i.e. without the need of building the package) by using [CuPy](https://docs.cupy.dev) implementations.
 
 ```sh
-pip install git+https://github.com/vais-ral/CCPi-Regularisation-Toolkit
+pip install git+https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit
 ```
 
 > [!WARNING]

Readme.md

@@ -2,6 +2,7 @@
 
 [![CI status](https://anvil.softeng-support.ac.uk/jenkins/buildStatus/icon?subject=master&job=CILsingle/CCPi-Regularisation-Toolkit)](https://anvil.softeng-support.ac.uk/jenkins/job/CILsingle/job/CCPi-Regularisation-Toolkit/lastBuild)
 [![conda version](https://anaconda.org/ccpi/ccpi-regulariser/badges/version.svg) ![conda date](https://anaconda.org/ccpi/ccpi-regulariser/badges/latest_release_date.svg) ![conda platforms](https://anaconda.org/ccpi/ccpi-regulariser/badges/platforms.svg) ![conda downloads](https://anaconda.org/ccpi/ccpi-regulariser/badges/downloads.svg)](https://anaconda.org/ccpi/ccpi-regulariser)
+[![License](https://img.shields.io/github/license/TomographicImaging/CCPi-Regularisation-Toolkit)](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit/blob/master/LICENSE)
 
 Iterative image reconstruction (IIR) methods frequently require regularisation to ensure convergence and make inverse problem well-posed. The CCPi-Regularisation Toolkit (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 problems. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided. With [CuPy](https://docs.cupy.dev/en/stable/index.html) dependency installed for Python, one can use regularisers directly without the need for explicit compilation. We recommend this option as the simplest to start with if you've got a GPU. This software can also be used by running in parallel across multiple GPU devices on a PC or a cluster compute node.
 
@@ -20,24 +21,24 @@ Iterative image reconstruction (IIR) methods frequently require regularisation t
 
 ### Single-channel (scalar)
 
-1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) **2D/3D CPU/GPU + CuPy** (Ref. *1*)
-2. Fast-Gradient-Projection (FGP) Total Variation **2D/3D CPU/GPU** (Ref. *2*)
-3. Split-Bregman (SB) Total Variation **2D/3D CPU/GPU** (Ref. *5*)
-4. Primal-Dual (PD) Total Variation **2D/3D CPU/GPU + CuPy** (Ref. *13*)
-5. Total Generalised Variation (TGV) model for higher-order regularisation **2D/3D CPU/GPU** (Ref. *6,13*)
-6. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *8*)
-7. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *9*)
-8. A joint ROF-LLT (Lysaker-Lundervold-Tai) model for higher-order regularisation **2D/3D CPU/GPU** (Ref. *10,11*)
-9. Nonlocal Total Variation regularisation (GS fixed point iteration) **2D CPU/GPU** (Ref. *12*)
+1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) **2D/3D CPU/GPU + CuPy**[^1]
+2. Fast-Gradient-Projection (FGP) Total Variation **2D/3D CPU/GPU**[^2]
+3. Split-Bregman (SB) Total Variation **2D/3D CPU/GPU**[^5]
+4. Primal-Dual (PD) Total Variation **2D/3D CPU/GPU + CuPy**[^13]
+5. Total Generalised Variation (TGV) model for higher-order regularisation **2D/3D CPU/GPU**[^6][^13]
+6. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU**[^8]
+7. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU**[^9]
+8. A joint ROF-LLT (Lysaker-Lundervold-Tai) model for higher-order regularisation **2D/3D CPU/GPU**[^10][^11]
+9. Nonlocal Total Variation regularisation (GS fixed point iteration) **2D CPU/GPU**[^12]
 
 ### Multi-channel (vectorial)
 
-1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,4,2*)
-2. Total Nuclear Variation (TNV) penalty **2D+channels CPU** (Ref. *7*)
+1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU**[^3][^4][^2]
+2. Total Nuclear Variation (TNV) penalty **2D+channels CPU**[^7]
 
 ## Installation
 
-The package comes as a [CMake](https://cmake.org) project and additional wrappers for Python and Matlab. Please see more detailed [Installation](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/Installation.md) information.
+The package comes as a [CMake](https://cmake.org) project and additional wrappers for Python and Matlab. Please see more detailed [Installation](./Installation.md) information.
 
 ### Python binaries
 
@@ -55,25 +56,23 @@ One can also use some of the GPU modules with the provided [CuPy](https://docs.c
 conda install -c httomo ccpi-regularisation-cupy
 ```
 
-Once installed please see [Demos](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/demos/demo_gpu_regularisers3D_CuPy.py). Please note that not all modules are yet supported as this is an ongoing development. One can install both CuPy-driven and the `ccpi-regulariser` packge in one environment, but please be aware that the functions carry the identical names.
+Once installed please see [Demos](./demos/demo_gpu_regularisers3D_CuPy.py). Please note that not all modules are yet supported as this is an ongoing development. One can install both CuPy-driven and the `ccpi-regulariser` package in one environment, but please be aware that the functions carry the identical names.
 
 ## References
 
-### Implemented methods
-
-1. [Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4)](https://www.sciencedirect.com/science/article/pii/016727899290242F)
-2. [Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11)](https://doi.org/10.1109/TIP.2009.2028250)
-3. [Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3)](https://doi.org/10.1137/15M1047325)
-4. [Kazantsev, D., Jørgensen, J.S., Andersen, M., Lionheart, W.R., Lee, P.D. and Withers, P.J., 2018. Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography. Inverse Problems, 34(6)](https://doi.org/10.1088/1361-6420/aaba86) **Results can be reproduced using the following** [SOFTWARE](https://github.com/dkazanc/multi-channel-X-ray-CT)
-5. [Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2)](https://doi.org/10.1137/080725891)
-6. [Bredies, K., Kunisch, K. and Pock, T., 2010. Total generalized variation. SIAM Journal on Imaging Sciences, 3(3)](https://doi.org/10.1137/090769521)
-7. [Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1)](https://doi.org/10.1137/15M102873X)
-8. [Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3)](https://doi.org/10.1109/83.661192)
-9. [Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2)](https://doi.org/10.1007/s11263-010-0330-1)
-10. [Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12)](https://doi.org/10.1109/TIP.2003.819229)
-11. [Kazantsev, D., Guo, E., Phillion, A.B., Withers, P.J. and Lee, P.D., 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9)](https://doi.org/10.1088/1361-6501/aa7fa8)
-12. [Abderrahim E., Lezoray O. and Bougleux S. 2008. Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Processing 17(7), pp. 1047-1060.](https://ieeexplore.ieee.org/document/4526700)
-13. [Chambolle, A. and Pock, T., 2010. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of mathematical imaging and vision 40(1)](https://doi.org/10.1007/s10851-010-0251-1)
+[^1]: [Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4)](https://www.sciencedirect.com/science/article/pii/016727899290242F)
+[^2]: [Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11)](https://doi.org/10.1109/TIP.2009.2028250)
+[^3]: [Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3)](https://doi.org/10.1137/15M1047325)
+[^4]: [Kazantsev, D., Jørgensen, J.S., Andersen, M., Lionheart, W.R., Lee, P.D. and Withers, P.J., 2018. Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography. Inverse Problems, 34(6)](https://doi.org/10.1088/1361-6420/aaba86) **Results can be reproduced using the following** [SOFTWARE](https://github.com/dkazanc/multi-channel-X-ray-CT)
+[^5]: [Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2)](https://doi.org/10.1137/080725891)
+[^6]: [Bredies, K., Kunisch, K. and Pock, T., 2010. Total generalized variation. SIAM Journal on Imaging Sciences, 3(3)](https://doi.org/10.1137/090769521)
+[^7]: [Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1)](https://doi.org/10.1137/15M102873X)
+[^8]: [Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3)](https://doi.org/10.1109/83.661192)
+[^9]: [Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2)](https://doi.org/10.1007/s11263-010-0330-1)
+[^10]: [Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12)](https://doi.org/10.1109/TIP.2003.819229)
+[^11]: [Kazantsev, D., Guo, E., Phillion, A.B., Withers, P.J. and Lee, P.D., 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9)](https://doi.org/10.1088/1361-6501/aa7fa8)
+[^12]: [Abderrahim E., Lezoray O. and Bougleux S. 2008. Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Processing 17(7), pp. 1047-1060.](https://ieeexplore.ieee.org/document/4526700)
+[^13]: [Chambolle, A. and Pock, T., 2010. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of mathematical imaging and vision 40(1)](https://doi.org/10.1007/s10851-010-0251-1)
 
 ### Software (please cite if used)
 
@@ -87,7 +86,7 @@ Once installed please see [Demos](https://github.com/vais-ral/CCPi-Regularisatio
 
 ### License
 
-[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+[![License](https://img.shields.io/github/license/TomographicImaging/CCPi-Regularisation-Toolkit)](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit/blob/master/LICENSE)
 
 ### Acknowledgments
 

demos/SoftwareX_supp/Readme.md

@@ -1,7 +1,7 @@
 # Software X journal publication supporting files
 
 ## Description:
-The scripts support [publication](https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/demos/SoftwareX_supp/paper/1-s2.0-S2352711018301912-main.pdf) in Software X journal [1] to ensure reproducibility of the research. The scripts linked with the data which is shared at [Zenodo](https://doi.org/10.5281/zenodo.2578893). 
+The scripts support [publication](./paper/1-s2.0-S2352711018301912-main.pdf) in Software X journal [1] to ensure reproducibility of the research. The scripts linked with the data which is shared at [Zenodo](https://doi.org/10.5281/zenodo.2578893).
 
 ## Data:
 Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
@@ -11,15 +11,14 @@ Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
 2. [ToMoBAR](https://github.com/dkazanc/ToMoBAR): `conda install -c dkazanc tomobar`
 3. [Tomophantom](https://github.com/dkazanc/TomoPhantom): `conda install tomophantom -c ccpi`
 
-## Files description: 
+## Files description:
 - `Demo_SimulData_SX.py` - simulates 3D projection data using [Tomophantom](https://github.com/dkazanc/TomoPhantom) software. One can skip this module if the data is taken from [Zenodo](https://doi.org/10.5281/zenodo.2578893)
-- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped. 
+- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped.
 - `Demo_SimulData_Recon_SX.py` - using established regularisation parameters, one runs iterative reconstruction
-- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed. 
+- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed.
 
 ### References:
-[1] [Kazantsev, D., Pasca, E., Turner, M.J. and Withers, P.J., 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.](https://www.sciencedirect.com/science/article/pii/S2352711018301912) 
+[1] [Kazantsev, D., Pasca, E., Turner, M.J. and Withers, P.J., 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.](https://www.sciencedirect.com/science/article/pii/S2352711018301912)
 
 ### Acknowledgments:
 CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group, STFC SCD software developers and Diamond Light Source (DLS). Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
-