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| author | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-05-11 12:44:18 +0100 |
|---|---|---|
| committer | Daniil Kazantsev <dkazanc@hotmail.com> | 2018-05-11 12:44:18 +0100 |
| commit | 0dd1cadcfead9a2a5f225e1500c97cc00a8068d6 (patch) | |
| tree | 7026566f4b7ec41d5039608f53bc734d2763a540 /Wrappers/Python/ccpi | |
| parent | 4f0fc6601200aadab9502b602000dc6b74539c6b (diff) | |
| download | framework-plugins-0dd1cadcfead9a2a5f225e1500c97cc00a8068d6.tar.gz framework-plugins-0dd1cadcfead9a2a5f225e1500c97cc00a8068d6.tar.bz2 framework-plugins-0dd1cadcfead9a2a5f225e1500c97cc00a8068d6.tar.xz framework-plugins-0dd1cadcfead9a2a5f225e1500c97cc00a8068d6.zip | |
some fixes regarding denoising
Diffstat (limited to 'Wrappers/Python/ccpi')
| -rw-r--r-- | Wrappers/Python/ccpi/plugins/regularisers.py | 16 |
1 files changed, 9 insertions, 7 deletions
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index e9c88a4..6d865cc 100644 --- a/Wrappers/Python/ccpi/plugins/regularisers.py +++ b/Wrappers/Python/ccpi/plugins/regularisers.py @@ -25,7 +25,6 @@ from ccpi.optimisation.ops import Operator import numpy as np - class _ROF_TV_(Operator): def __init__(self,lambdaReg,iterationsTV,tolerance,time_marchstep,device): # set parameters @@ -33,9 +32,10 @@ class _ROF_TV_(Operator): self.iterationsTV = iterationsTV self.time_marchstep = time_marchstep self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x): + def __call__(self,x,x1,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x1.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : ROF_TV, \ @@ -60,9 +60,10 @@ class _FGP_TV_(Operator): self.nonnegativity = nonnegativity self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x): + def __call__(self,x,x1,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x1.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : FGP_TV, \ @@ -93,9 +94,10 @@ class _SB_TV_(Operator): self.methodTV = methodTV self.printing = printing self.device = device # string for 'cpu' or 'gpu' - def __call__(self,x): + def __call__(self,x,typeEnergy): # evaluate objective function of TV gradient - EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2) + # typeEnergy is either 1 (LS + TV for denoising) or 2 (just TV fidelity) + EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, typeEnergy) return EnergyValTV def prox(self,x,Lipshitz): pars = {'algorithm' : SB_TV, \ |