diff options
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 34 |
1 files changed, 21 insertions, 13 deletions
@@ -1,8 +1,9 @@ # 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> @@ -10,7 +11,7 @@ can also be used as image denoising iterative filters. The core modules are writ ## Prerequisites: - * MATLAB (www.mathworks.com/products/matlab/) OR + * [MATLAB](www.mathworks.com/products/matlab/) OR * Python (tested ver. 3.5); Cython * C compilers * nvcc (CUDA SDK) compilers @@ -18,13 +19,14 @@ can also be used as image denoising iterative filters. The core modules are writ ## Package modules (regularisers): ### Single-channel -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) +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) -2. Total Nuclear Variation (TNV) penalty [2D+channels CPU]; (Ref. 5) +1. Fast-Gradient-Projection (FGP) Directional Total Variation **2D/3D CPU/GPU** (Ref. *3,2*) +2. Total Nuclear Variation (TNV) penalty **2D+channels CPU** (Ref. *5*) ## Installation: @@ -48,11 +50,17 @@ can also be used as image denoising iterative filters. The core modules are writ ``` ### References: -1. Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268. -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), pp.2419-2434. -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. +*1. Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268.* + +*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), pp.2419-2434.* + +*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) |