diff options
Diffstat (limited to 'Wrappers/Python/ccpi/plugins/regularisers.py')
| -rw-r--r-- | Wrappers/Python/ccpi/plugins/regularisers.py | 7 |
1 files changed, 3 insertions, 4 deletions
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py index e9c88a4..46464a9 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 @@ -36,7 +35,7 @@ class _ROF_TV_(Operator): def __call__(self,x): # 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) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : ROF_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ @@ -63,7 +62,7 @@ class _FGP_TV_(Operator): def __call__(self,x): # 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) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : FGP_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ @@ -96,7 +95,7 @@ class _SB_TV_(Operator): def __call__(self,x): # 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) - return EnergyValTV + return 0.5*EnergyValTV[0] def prox(self,x,Lipshitz): pars = {'algorithm' : SB_TV, \ 'input' : np.asarray(x.as_array(), dtype=np.float32),\ |