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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-05-01 09:44:07 +0100 |
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| committer | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-05-01 09:44:07 +0100 |
| commit | 09eb48ffbb4ad699e2eefd25678e10dc59d6a177 (patch) | |
| tree | 8cebd50609d4ba4a634a8c91252d205580b56b4c /Readme.md | |
| parent | 307d0459f6f22ff07e9d0b8d4090a27ba91cddd0 (diff) | |
| download | regularization-09eb48ffbb4ad699e2eefd25678e10dc59d6a177.tar.gz regularization-09eb48ffbb4ad699e2eefd25678e10dc59d6a177.tar.bz2 regularization-09eb48ffbb4ad699e2eefd25678e10dc59d6a177.tar.xz regularization-09eb48ffbb4ad699e2eefd25678e10dc59d6a177.zip | |
new inpainters
Diffstat (limited to 'Readme.md')
| -rw-r--r-- | Readme.md | 27 |
1 files changed, 17 insertions, 10 deletions
@@ -16,17 +16,22 @@ the toolkit can be used independently to solve image denoising problems. The cor * C compilers * nvcc (CUDA SDK) compilers -## Package modules (regularisers): +## Package modules: -### Single-channel +### Single-channel (denoising): 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*) +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*) + +### 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*) + +### Inpainting: +1. Linear and nonlinear diffusion (explicit PDE minimisation scheme) **2D/3D CPU** (Ref. *7*) +2. Iterative nonlocal vertical marching method **2D CPU** -### 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: @@ -56,11 +61,13 @@ the toolkit can be used independently to solve image denoising problems. The cor *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.* +*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* + +*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.* -*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. 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.* +*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.* ### License: [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) |