NonlDiff added 2D CPU/CUDA

1 files changed, 5 insertions, 2 deletions

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 # 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 consist of 2D/3D regularisation modules for single-channel and multi-channel reconstruction problems. The modules especially suited for IIR, however,
-can also be used as image denoising iterative filters. The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** 
+CCPi-RGL software consist of 2D/3D regularisation modules for single-channel and multi-channel reconstruction problems. The regularisation modules are well-suited for 
+[splitting algorithms](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method#Alternating_direction_method_of_multipliers), of ADMM or FISTA type. Furthermore, 
+the toolkit can be used independently to solve image denoising 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="docs/images/probl.png" height="225"><br>  
@@ -21,6 +22,7 @@ can also be used as image denoising iterative filters. The core modules are writ
 1. Rudin-Osher-Fatemi (ROF) Total Variation (explicit PDE minimisation scheme) [2D/3D CPU/GPU]; (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. 4)
+4. Linear and nonlinear diffusion (explicit PDE minimisation scheme) [2D/3D CPU/GPU]; (Ref. 6)
 
 ### Multi-channel
 1. Fast-Gradient-Projection (FGP) Directional Total Variation [2D/3D CPU/GPU]; (Ref. 3,2)
@@ -53,6 +55,7 @@ can also be used as image denoising iterative filters. The core modules are writ
 3. Ehrhardt, M.J. and Betcke, M.M., 2016. Multicontrast MRI reconstruction with structure-guided total variation. SIAM Journal on Imaging Sciences, 9(3), pp.1084-1106.
 4. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
 5. 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), pp.116-151.
+6. Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
 
 ### License:
 [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0)