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Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 23 |
1 files changed, 13 insertions, 10 deletions
@@ -19,15 +19,16 @@ 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. *5*) -4. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *7*) -5. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *8*) +4. Total Generilised Variation (TGV) model **2D CPU/GPU** (Ref. *6*) +5. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU/GPU** (Ref. *8*) +6. Anisotropic Fourth-Order Diffusion (explicit PDE minimisation) **2D/3D CPU/GPU** (Ref. *9*) ### Multi-channel (denoising): 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. *6*) +2. Total Nuclear Variation (TNV) penalty **2D+channels CPU** (Ref. *7*) ### Inpainting: -1. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU** (Ref. *7*) +1. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU** (Ref. *8*) 2. Iterative nonlocal vertical marching method **2D CPU** @@ -35,12 +36,12 @@ ### Python (conda-build) ``` - export CIL_VERSION=0.9.2 + export CIL_VERSION=0.9.4 conda build recipes/regularisers --numpy 1.12 --python 3.5 - conda install cil_regulariser=0.9.2 --use-local --force + conda install cil_regulariser=0.9.4 --use-local --force cd Wrappers/Python conda build conda-recipe --numpy 1.12 --python 3.5 - conda install ccpi-regulariser=0.9.2 --use-local --force + conda install ccpi-regulariser=0.9.4 --use-local --force cd demos/ python demo_cpu_regularisers.py # to run CPU demo python demo_gpu_regularisers.py # to run GPU demo @@ -63,11 +64,13 @@ *5. [Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.](https://doi.org/10.1137/080725891)* -*6. [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.](https://doi.org/10.1137/15M102873X)* +*6. [Bredies, K., Kunisch, K. and Pock, T., 2010. Total generalized variation. SIAM Journal on Imaging Sciences, 3(3), pp.492-526.](https://doi.org/10.1137/090769521) -*7. [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.](https://doi.org/10.1109/83.661192)* +*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), pp.116-151.](https://doi.org/10.1137/15M102873X) -*8. [Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.](https://doi.org/10.1007/s11263-010-0330-1)* +*8. [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.](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), pp.177-191.](https://doi.org/10.1007/s11263-010-0330-1)* ### License: [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) |