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
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
|
# -*- coding: utf-8 -*-
# Copyright 2019 Science Technology Facilities Council
# Copyright 2019 University of Manchester
#
# This work is part of the Core Imaging Library developed by Science Technology
# Facilities Council and University of Manchester
#
# 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.txt
#
# 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.
from ccpi.filters import regularisers
from ccpi.filters.cpu_regularisers import TV_ENERGY
from ccpi.framework import DataContainer
from ccpi.optimisation.functions import Function
import numpy as np
import warnings
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'
self.tolerance = tolerance
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 proximal(self,x,tau, out = None):
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,\
'tolerance':self.tolerance}
res , info = regularisers.ROF_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'], pars['tolerance'], self.device)
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return 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 proximal(self,x,tau, out=None):
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}
res , info = regularisers.FGP_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
def convex_conjugate(self,x):
return 0.0
class TGV(Function):
def __init__(self, regularisation_parameter, alpha1, alpha2, iter_TGV, LipshitzConstant, torelance, device ):
self.regularisation_parameter = regularisation_parameter
self.alpha1 = alpha1
self.alpha2 = alpha2
self.iter_TGV = iter_TGV
self.LipshitzConstant = LipshitzConstant
self.torelance = torelance
self.device = device
def __call__(self,x):
warnings.warn("{}: the __call__ method is not currently implemented. Returning 0.".format(self.__class__.__name__))
# TODO this is not correct, need a TGV energy same as TV
return 0.0
def proximal(self, x, tau, out=None):
pars = {'algorithm' : TGV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularisation_parameter':self.regularisation_parameter, \
'alpha1':self.alpha1,\
'alpha0':self.alpha2,\
'number_of_iterations' :self.iter_TGV ,\
'LipshitzConstant' :self.LipshitzConstant ,\
'tolerance_constant':self.torelance}
res , info = regularisers.TGV(pars['input'],
pars['regularisation_parameter'],
pars['alpha1'],
pars['alpha0'],
pars['number_of_iterations'],
pars['LipshitzConstant'],
pars['tolerance_constant'],self.device)
# info: return number of iteration and reached tolerance
# https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
# Stopping Criteria || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
def convex_conjugate(self, x):
# TODO this is not correct
return 0.0
class LLT_ROF(Function):
# pars = {'algorithm' : LLT_ROF, \
# 'input' : noisyVol,\
# 'regularisation_parameterROF':0.01, \
# 'regularisation_parameterLLT':0.008, \
# 'number_of_iterations' : 500 ,\
# 'time_marching_parameter' :0.001 ,\
# 'tolerance_constant':1e-06}
def __init__(self, regularisation_parameterROF,
regularisation_parameterLLT,
iter_LLT_ROF, time_marching_parameter, torelance, device ):
self.regularisation_parameterROF = regularisation_parameterROF
self.regularisation_parameterLLT = regularisation_parameterLLT
self.iter_LLT_ROF = iter_LLT_ROF
self.time_marching_parameter = time_marching_parameter
self.torelance = torelance
self.device = device
def __call__(self,x):
warnings.warn("{}: the __call__ method is not currently implemented. Returning 0.".format(self.__class__.__name__))
# TODO this is not correct, need a TGV energy same as TV
return 0.0
def proximal(self, x, tau, out=None):
pars = {'algorithm' : LLT_ROF, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularisation_parameterROF':self.regularisation_parameterROF, \
'regularisation_parameterLLT':self.regularisation_parameterLLT,
'number_of_iterations' :self.iter_LLT_ROF ,\
'time_marching_parameter': self.time_marching_parameter,\
'tolerance_constant':self.torelance}
res , info = regularisers.LLT_ROF(pars['input'],
pars['regularisation_parameterROF'],
pars['regularisation_parameterLLT'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['tolerance_constant'],self.device)
# info: return number of iteration and reached tolerance
# https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
# Stopping Criteria || u^k - u^(k-1) ||_{2} / || u^{k} ||_{2}
self.info = info
class FGP_dTV(Function):
def __init__(self, refdata, regularisation_parameter, iterations,
tolerance, eta_const, methodTV, nonneg, device='cpu'):
# set parameters
self.lambdaReg = regularisation_parameter
self.iterationsTV = iterations
self.tolerance = tolerance
self.methodTV = methodTV
self.nonnegativity = nonneg
self.device = device # string for 'cpu' or 'gpu'
self.refData = np.asarray(refdata.as_array(), dtype=np.float32)
self.eta = eta_const
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 proximal(self,x,tau, out=None):
pars = {'algorithm' : FGP_dTV, \
'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 ,\
'eta_const' : self.eta,\
'refdata':self.refData}
#inputData, refdata, regularisation_parameter, iterations,
# tolerance_param, eta_const, methodTV, nonneg, device='cpu'
res , info = regularisers.FGP_dTV(pars['input'],
pars['refdata'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['eta_const'],
pars['methodTV'],
pars['nonneg'],
self.device)
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
def convex_conjugate(self, x):
# TODO this is not correct
return 0.0
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 proximal(self,x,tau, out=None):
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}
res , info = regularisers.SB_TV(pars['input'],
pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['methodTV'], self.device)
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
class TNV(Function):
def __init__(self,regularisation_parameter,iterationsTNV,tolerance):
# set parameters
self.regularisation_parameter = regularisation_parameter
self.iterationsTNV = iterationsTNV
self.tolerance = tolerance
def __call__(self,x):
warnings.warn("{}: the __call__ method is not currently implemented. Returning 0.".format(self.__class__.__name__))
# evaluate objective function of TV gradient
return 0.0
def proximal(self,x,tau, out=None):
pars = {'algorithm' : TNV, \
'input' : np.asarray(x.as_array(), dtype=np.float32),\
'regularisation_parameter':self.regularisation_parameter, \
'number_of_iterations' :self.iterationsTNV,\
'tolerance_constant':self.tolerance}
res = regularisers.TNV(pars['input'],
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'])
#self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
|
