DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
device_projector='gpu')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using SIRT (tomobar)...")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# prepare dictionaries with parameters:
_data_ = {'projection_norm_data': sino_an} # data dictionary
_algorithm_ = {'iterations': 250}
SIRTrec_ideal = RectoolsIR.SIRT(_data_, _algorithm_) # ideal reconstruction
_data_ = {'projection_norm_data': noisy_zing_stripe} # data dictionary
SIRTrec_error = RectoolsIR.SIRT(_data_, _algorithm_) # error reconstruction
plt.figure()
plt.subplot(121)
plt.imshow(SIRTrec_ideal, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Ideal SIRT reconstruction (ASTRA)')
plt.subplot(122)
plt.imshow(SIRTrec_error, vmin=0, vmax=3, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Erroneous data SIRT Reconstruction (ASTRA)')
plt.show()
plt.figure()
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=1e-06, # tolerance to stop outer iterations earlier
device='gpu')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using SIRT (tomobar)...")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
iterationsSIRT = 250
SIRTrec_ideal = RectoolsIR.SIRT(sino_an,
iterationsSIRT) # ideal reconstruction
SIRTrec_error = RectoolsIR.SIRT(noisy_zing_stripe,
iterationsSIRT) # error reconstruction
plt.figure()
plt.subplot(121)
plt.imshow(SIRTrec_ideal, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Ideal SIRT reconstruction (ASTRA)')
plt.subplot(122)
plt.imshow(SIRTrec_error, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Erroneous data SIRT Reconstruction (ASTRA)')
plt.show()
plt.figure()
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
device_projector='gpu')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using SIRT (tomobar)...")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# prepare dictionaries with parameters:
_data_ = {'projection_norm_data': noisy_sino} # data dictionary
_algorithm_ = {'iterations': 250}
SIRTrec = RectoolsIR.SIRT(_data_, _algorithm_) # ideal reconstruction
plt.figure()
plt.imshow(SIRTrec, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('SIRT Reconstruction')
plt.show()
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing using FISTA method (tomobar)")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# prepare dictionaries with parameters:
_data_ = {'projection_norm_data': noisy_sino} # data dictionary
lc = RectoolsIR.powermethod(
_data_) # calculate Lipschitz constant (run once to initialise)
_algorithm_ = {'iterations': 350, 'lipschitz_const': lc}
