1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
|
# -*- coding: utf-8 -*-
# This work is part of the Core Imaging Library developed by
# Visual Analytics and Imaging System Group of the Science Technology
# Facilities Council, STFC
# Copyright 2018 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This requires CCPi-Regularisation toolbox to be installed
from ccpi.filters import regularisers
from ccpi.filters.cpu_regularisers import TV_ENERGY
from ccpi.framework import DataContainer
from ccpi.optimisation.funcs import Function
import numpy as np
class ROF_TV(Function):
def __init__(self,lambdaReg,iterationsTV,tolerance,time_marchstep,device):
# set parameters
self.lambdaReg = lambdaReg
self.iterationsTV = iterationsTV
self.time_marchstep = time_marchstep
self.device = device # string for 'cpu' or 'gpu'
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 0.5*EnergyValTV[0]
def prox(self,x,tau):
pars = {'algorithm' : ROF_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'time_marching_parameter':self.time_marchstep}
out = regularisers.ROF_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'], self.device)
return DataContainer(out)
class FGP_TV(Function):
def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,nonnegativity,printing,device):
# set parameters
self.lambdaReg = lambdaReg
self.iterationsTV = iterationsTV
self.tolerance = tolerance
self.methodTV = methodTV
self.nonnegativity = nonnegativity
self.printing = printing
self.device = device # string for 'cpu' or 'gpu'
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 0.5*EnergyValTV[0]
def prox(self,x,tau):
pars = {'algorithm' : FGP_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'tolerance_constant':self.tolerance,\
'methodTV': self.methodTV ,\
'nonneg': self.nonnegativity ,\
'printingOut': self.printing}
out = regularisers.FGP_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
pars['printingOut'], self.device)
return DataContainer(out)
class SB_TV(Function):
def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,printing,device):
# set parameters
self.lambdaReg = lambdaReg
self.iterationsTV = iterationsTV
self.tolerance = tolerance
self.methodTV = methodTV
self.printing = printing
self.device = device # string for 'cpu' or 'gpu'
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 0.5*EnergyValTV[0]
def prox(self,x,tau):
pars = {'algorithm' : SB_TV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularization_parameter':self.lambdaReg*tau, \
'number_of_iterations' :self.iterationsTV ,\
'tolerance_constant':self.tolerance,\
'methodTV': self.methodTV ,\
'printingOut': self.printing}
out = regularisers.SB_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'],
pars['printingOut'], self.device)
return DataContainer(out)
|
