From d1875172687fc854df35fa9bfc6ac07a148d7f18 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Sat, 12 May 2018 19:03:26 +0100
Subject: fixed objective2
---
Wrappers/Python/ccpi/plugins/regularisers.py | 9 +++------
1 file changed, 3 insertions(+), 6 deletions(-)
(limited to 'Wrappers/Python/ccpi')
diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py
index 9f4d3fc..46464a9 100644
--- a/Wrappers/Python/ccpi/plugins/regularisers.py
+++ b/Wrappers/Python/ccpi/plugins/regularisers.py
@@ -34,9 +34,8 @@ class _ROF_TV_(Operator):
self.device = device # string for 'cpu' or 'gpu'
def __call__(self,x):
# evaluate objective function of TV gradient
- # 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, 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),\
@@ -62,9 +61,8 @@ class _FGP_TV_(Operator):
self.device = device # string for 'cpu' or 'gpu'
def __call__(self,x):
# evaluate objective function of TV gradient
- # 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, 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),\
@@ -96,9 +94,8 @@ class _SB_TV_(Operator):
self.device = device # string for 'cpu' or 'gpu'
def __call__(self,x):
# evaluate objective function of TV gradient
- # 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, 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),\
--
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