def proximal_numpy(self, in_arr, tau, out = None):
res , info = regularisers.ROF_TV(in_arr,
self.alpha,
self.max_iteration,
self.time_marchstep,
self.tolerance,
self.device)
return res, info
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)
def proximal(self, x, tau, out=None):
in_arr = np.asarray(x.as_array(), dtype=np.float32, order='C')
res, info = regularisers.ROF_TV(in_arr, self.alpha, self.max_iteration,
self.time_marchstep, self.tolerance,
self.device)
self.info = info
if out is not None:
out.fill(res)
else:
out = x.copy()
out.fill(res)
return out
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