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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-04-17 12:58:28 +0100 |
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| committer | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-04-17 12:58:28 +0100 |
| commit | d0a33e4f941539ba44a071cfab75d7bf9543990f (patch) | |
| tree | ed825ba90ca17448ab07309435095f3612ffe703 /Readme.md | |
| parent | 7e556922a60e052d24c1e467df13423904729357 (diff) | |
| download | regularization-d0a33e4f941539ba44a071cfab75d7bf9543990f.tar.gz regularization-d0a33e4f941539ba44a071cfab75d7bf9543990f.tar.bz2 regularization-d0a33e4f941539ba44a071cfab75d7bf9543990f.tar.xz regularization-d0a33e4f941539ba44a071cfab75d7bf9543990f.zip | |
TNV module added
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 7 |
1 files changed, 5 insertions, 2 deletions
@@ -5,7 +5,7 @@ CCPi-RGL software consist of 2D/3D regularisation modules for single-channel and 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.** <div align="center"> - <img src="docs/images/probl.png" height="250"><br> + <img src="docs/images/probl.png" height="225"><br> </div> ## Prerequisites: @@ -24,6 +24,7 @@ can also be used as image denoising iterative filters. The core modules are writ ### 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) ## Installation: @@ -36,7 +37,8 @@ can also be used as image denoising iterative filters. The core modules are writ conda build conda-recipe --numpy 1.12 --python 3.5 conda install ccpi-regulariser=0.9.2 --use-local --force cd demos/ - python demo_cpu_regularisers.py.py # to run CPU demo + python demo_cpu_regularisers.py # to run CPU demo + python demo_gpu_regularisers.py # to run GPU demo ``` ### Matlab ``` @@ -50,6 +52,7 @@ can also be used as image denoising iterative filters. The core modules are writ 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. ### License: [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) |