np_meas = matlab_val
#np_mean = 1#np.mean(np_meas)
#np_meas = np_meas/np_mean
#print("Mean of the MEasurement is: "+str(np_mean))
'''Define Cost-function'''
# VERY VERY Important to add 0. and 1. - otherwise it gets converted to float!
tf_global_phase = tf.Variable(np_global_phase, tf.float32,
name='var_phase') # (0.902339905500412
tf_global_abs = tf.Variable(np_global_abs, tf.float32,
name='var_abs') #0.36691132
'''REGULARIZER'''
# Total Variation
print('We are using TV - Regularization')
tf_tvloss = muscat.tf_lambda_tv * reg.Reg_TV(
muscat.TF_obj,
BetaVals=[muscat.dx, muscat.dy, muscat.dz],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
tf_tvloss += muscat.tf_lambda_tv * reg.Reg_TV(
muscat.TF_obj_absorption,
BetaVals=[muscat.dx, muscat.dy, muscat.dz],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
'''Negativity Constraint'''
tf_negsqrloss = lambda_neg * reg.Reg_NegSqr(muscat.TF_obj)
tf_negsqrloss += lambda_neg * reg.Reg_NegSqr(muscat.TF_obj_absorption)
# Correc the fwd model - not good here!
tf_norm = tf.complex(tf_global_phase, tf_global_abs)
tf_fwd_corrected = tf_fwd + tf_norm
name='Object_Variable_Imag')
''' Create Model Instance'''
tf_fwd = muscat.computemodel()
tf_fwd = tf_fwd + 1j
'''experiments.regularizer'''
# Total Variation
tf_regloss = tf.constant(0.)
if (experiments.regularizer == 'TV'):
# TV experiments.regularizer
print('We are using TV - Regularization'
) # (tfin, Eps=1e-15, doubleSided=False,regDataType=None)
if (0):
mysqrt_real = reg.Reg_TV(
muscat.TF_obj,
BetaVals=[1, 1, 1],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
tf_regloss_real = tf.reduce_mean(mysqrt_real)
mysqrt_imag = reg.Reg_TV(
muscat.TF_obj_absorption,
BetaVals=[1, 1, 1],
epsR=muscat.tf_eps,
is_circ=True
) #Alernatively tf_total_variation_regularization # total_variation
tf_regloss_imag = tf.reduce_mean(mysqrt_imag)
if is_absorption:
tf_regloss = muscat.tf_lambda_reg * (tf_regloss_real +
tf_regloss_imag)
else:
tf_regloss = muscat.tf_lambda_reg * (tf_regloss_real)
if (experiments.regularizer == 'TV'):
# TV experiments.regularizer
print('We are using TV - Regularization'
) # (tfin, Eps=1e-15, doubleSided=False,regDataType=None)
# TF_obj_tmp = tf_helper.extract(tf.cast(muscat.TF_obj, tf.float32), muscat.mysize)
#TF_obj_absorption_tmp = tf_helper.extract(tf.cast(muscat.TF_obj_absorption, tf.float32), muscat.mysize)
#tf_regloss = muscat.tf_lambda_reg*reg.Reg_TV_RH(muscat.TF_obj, Eps=muscat.tf_eps) #Alernatively tf_total_variation_regularization # total_variation
#tf_regloss += muscat.tf_lambda_reg*reg.Reg_TV_RH(muscat.TF_obj_absorption, Eps=muscat.tf_eps) #Alernatively tf_total_variation_regularization # total_variation
# [muscat.dx,muscat.dy,muscat.dz]
tf_regloss += muscat.tf_lambda_reg * reg.Reg_TV(
muscat.TF_obj,
BetaVals=[
muscat.myparams.dz, muscat.myparams.dx, muscat.myparams.dy
],
epsR=muscat.tf_eps,
is_circ=True,
is_z=True
) #Alernatively tf_total_variation_regularization # total_variation
if (is_absorption):
tf_regloss += muscat.tf_lambda_reg * reg.Reg_TV(
muscat.TF_obj_absorption,
BetaVals=[
muscat.myparams.dz, muscat.myparams.dx,
muscat.myparams.dy
],
epsR=muscat.tf_eps,
is_circ=True,
is_z=True
) #Alernatively tf_total_variation_regularization # total_variation
