def do_test_with_fidelity(self, fidelity):
alpha = self.alpha
# F = BlockFunction(alpha * MixedL21Norm(),fidelity)
G = fidelity
K = self.K
F = self.F
admm = LADMM(f=G,
g=F,
operator=K,
tau=self.tau,
sigma=self.sigma,
max_iteration=100,
update_objective_interval=10)
admm.run(1, verbose=0)
admm_noaxpby = LADMM(f=G,
g=F,
operator=K,
tau=self.tau,
sigma=self.sigma,
max_iteration=100,
update_objective_interval=10,
use_axpby=False)
admm_noaxpby.run(1, verbose=0)
np.testing.assert_array_almost_equal(admm.solution.as_array(),
admm_noaxpby.solution.as_array())
def test_compare_with_PDHG(self):
# Load an image from the CIL gallery.
data = dataexample.SHAPES.get()
ig = data.geometry
# Add gaussian noise
noisy_data = applynoise.gaussian(data, seed=10, var=0.005)
# TV regularisation parameter
alpha = 1
# fidelity = 0.5 * L2NormSquared(b=noisy_data)
# fidelity = L1Norm(b=noisy_data)
fidelity = KullbackLeibler(b=noisy_data, use_numba=False)
# Setup and run the PDHG algorithm
F = BlockFunction(alpha * MixedL21Norm(), fidelity)
G = ZeroFunction()
K = BlockOperator(GradientOperator(ig), IdentityOperator(ig))
# Compute operator Norm
normK = K.norm()
# Primal & dual stepsizes
sigma = 1. / normK
tau = 1. / normK
pdhg = PDHG(f=F,
g=G,
operator=K,
tau=tau,
sigma=sigma,
max_iteration=100,
update_objective_interval=10)
pdhg.run(verbose=0)
sigma = 1
tau = sigma / normK**2
admm = LADMM(f=G,
g=F,
operator=K,
tau=tau,
sigma=sigma,
max_iteration=100,
update_objective_interval=10)
admm.run(verbose=0)
from cil.utilities.quality_measures import psnr
if debug_print:
print("PSNR", psnr(admm.solution, pdhg.solution))
np.testing.assert_almost_equal(psnr(admm.solution, pdhg.solution),
84.46678222768597,
decimal=4)