plt.figure()
plt.semilogy(FISTA_alg.objective)
plt.title('FISTA Least squares criterion')
plt.show()
# FISTA can also solve regularised forms by specifying a second function object
# such as 1-norm regularisation with choice of regularisation parameter lam:
# Create 1-norm function object
lam = 1.0
g0 = lam * L1Norm()
# Run FISTA for least squares plus 1-norm function.
FISTA_alg1 = FISTA()
FISTA_alg1.set_up(x_init=x_init, f=f, g=g0)
FISTA_alg1.max_iteration = 2000
FISTA_alg1.run(opt['iter'])
x_FISTA1 = FISTA_alg1.get_output()
plt.figure()
plt.imshow(x_FISTA1.array)
plt.title('FISTA LS+L1Norm reconstruction')
plt.colorbar()
plt.show()
plt.figure()
plt.semilogy(FISTA_alg1.objective)
plt.title('FISTA LS+L1norm criterion')
plt.show()
axarro[k].imshow(z.as_array()[k], vmin=0, vmax=3500)
plt.show()
# Using the test data b, different reconstruction methods can now be set up as
# demonstrated in the rest of this file. In general all methods need an initial
# guess and some algorithm options to be set:
x_init = ig.allocate(0.0)
opt = {'tol': 1e-4, 'iter': 200}
# Create least squares object instance with projector, test data and a constant
# coefficient of 0.5. Note it is least squares over all channels:
#f = Norm2Sq(Aop,b,c=0.5)
f = FunctionOperatorComposition(L2NormSquared(b=b), Aop)
# Run FISTA for least squares without regularization
FISTA_alg = FISTA()
FISTA_alg.set_up(x_init=x_init, f=f, g=ZeroFunction())
FISTA_alg.max_iteration = 2000
FISTA_alg.run(opt['iter'])
x_FISTA = FISTA_alg.get_output()
# Display reconstruction and criterion
ff0, axarrf0 = plt.subplots(1, numchannels)
for k in numpy.arange(3):
axarrf0[k].imshow(x_FISTA.as_array()[k], vmin=0, vmax=2.5)
plt.show()
plt.figure()
plt.semilogy(FISTA_alg.objective)
plt.title('Criterion vs iterations, least squares')
plt.show()
pdhg = PDHG()
pdhg.set_up(f=f, g=g, operator=operator, tau=tau, sigma=sigma)
pdhg.max_iteration = 1000
pdhg.update_objective_interval = 100
pdhg.run(1000, verbose=True)
#%%
###############################################################################
# Setup and run the FISTA algorithm
print("Running FISTA reconstruction")
fidelity = FunctionOperatorComposition(L2NormSquared(b=sinogram), Aop)
regularizer = ZeroFunction()
fista = FISTA()
fista.set_up(x_init=x_init, f=fidelity, g=regularizer)
fista.max_iteration = 500
fista.update_objective_interval = 100
fista.run(500, verbose=True)
#%% Show results
plt.figure(figsize=(10, 10))
plt.suptitle('Reconstructions ', fontsize=16)
plt.subplot(2, 2, 1)
plt.imshow(cgls.get_output().as_array())
plt.colorbar()
plt.title('CGLS reconstruction')
plt.subplot(2, 2, 2)