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| author | Daniil Kazantsev <dkazanc3@googlemail.com> | 2018-05-12 21:39:07 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-05-12 21:39:07 +0100 |
| commit | f62562b93d0070211fc7fff3ad7d3c144b828a80 (patch) | |
| tree | c09b8386a880f2f6dd158fecdbf62d540813119c /Wrappers/Python/ccpi | |
| parent | 992146ad44767f9f34515393b608ec2ca0304cd1 (diff) | |
| parent | 653e0adc87c255a392f30590af446fc78043e194 (diff) | |
| download | framework-plugins-f62562b93d0070211fc7fff3ad7d3c144b828a80.tar.gz framework-plugins-f62562b93d0070211fc7fff3ad7d3c144b828a80.tar.bz2 framework-plugins-f62562b93d0070211fc7fff3ad7d3c144b828a80.tar.xz framework-plugins-f62562b93d0070211fc7fff3ad7d3c144b828a80.zip | |
Merge pull request #11 from vais-ral/RGLTK_TV_denoising_demo
Rgltk tv denoising demo
Diffstat (limited to 'Wrappers/Python/ccpi')
| -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),\ |