def setup(data, dnoise):
if dnoise == 's&p':
n1 = applynoise.saltnpepper(data,
salt_vs_pepper=0.9,
amount=0.2,
seed=10)
elif dnoise == 'poisson':
scale = 5
n1 = applynoise.poisson(data.as_array() / scale,
seed=10) * scale
elif dnoise == 'gaussian':
n1 = applynoise.gaussian(data.as_array(), seed=10)
else:
raise ValueError('Unsupported Noise ', noise)
noisy_data = ig.allocate()
noisy_data.fill(n1)
# Regularisation Parameter depending on the noise distribution
if dnoise == 's&p':
alpha = 0.8
elif dnoise == 'poisson':
alpha = 1
elif dnoise == 'gaussian':
alpha = .3
# fidelity
if dnoise == 's&p':
g = L1Norm(b=noisy_data)
elif dnoise == 'poisson':
g = KullbackLeibler(b=noisy_data)
elif dnoise == 'gaussian':
g = 0.5 * L2NormSquared(b=noisy_data)
return noisy_data, alpha, g
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)
def setUp(self):
print ("SETUP", np.version.version)
if has_skimage:
id_coins = dataexample.CAMERA.get()
id_coins_noisy = noise.gaussian(id_coins, var=0.05, seed=10)
ig = id_coins.geometry.copy()
dc1 = ig.allocate('random')
dc2 = ig.allocate('random')
self.dc1 = dc1
self.dc2 = dc2
self.id_coins = id_coins
self.id_coins_noisy = id_coins_noisy
def test_compare_regularisation_toolkit_tomophantom(self):
print("Compare CIL_FGP_TV vs CCPiReg_FGP_TV no tolerance (3D)")
print("Building 3D phantom using TomoPhantom software")
model = 13 # select a model number from the library
N_size = 64 # Define phantom dimensions using a scalar value (cubic phantom)
path = os.path.dirname(tomophantom.__file__)
path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
#This will generate a N_size x N_size x N_size phantom (3D)
phantom_tm = TomoP3D.Model(model, N_size, path_library3D)
ig = ImageGeometry(N_size, N_size, N_size)
data = ig.allocate()
data.fill(phantom_tm)
noisy_data = noise.gaussian(data, seed=10)
alpha = 0.1
iters = 1000
print("Use tau as an array of ones")
# CIL_TotalVariation no tolerance
g_CIL = alpha * TotalVariation(iters, tolerance=None, info=True)
res1 = g_CIL.proximal(noisy_data, ig.allocate(1.))
t0 = timer()
res1 = g_CIL.proximal(noisy_data, ig.allocate(1.))
t1 = timer()
print(t1 - t0)
# CCPi Regularisation toolkit high tolerance
r_alpha = alpha
r_iterations = iters
r_tolerance = 1e-9
r_iso = 0
r_nonneg = 0
r_printing = 0
g_CCPI_reg_toolkit = CCPiReg_FGP_TV(r_alpha, r_iterations, r_tolerance,
r_iso, r_nonneg, r_printing, 'cpu')
t2 = timer()
res2 = g_CCPI_reg_toolkit.proximal(noisy_data, 1.)
t3 = timer()
print(t3 - t2)
np.testing.assert_array_almost_equal(res1.as_array(),
res2.as_array(),
decimal=3)
# CIL_FGP_TV no tolerance
#g_CIL = FGP_TV(ig, alpha, iters, tolerance=None, info=True)
g_CIL.tolerance = None
t0 = timer()
res1 = g_CIL.proximal(noisy_data, 1.)
t1 = timer()
print(t1 - t0)
###################################################################
###################################################################
###################################################################
###################################################################
data = dataexample.PEPPERS.get(size=(256, 256))
ig = data.geometry
ag = ig
noisy_data = noise.gaussian(data, seed=10)
alpha = 0.1
iters = 1000
# CIL_FGP_TV no tolerance
g_CIL = alpha * TotalVariation(iters, tolerance=None)
t0 = timer()
res1 = g_CIL.proximal(noisy_data, 1.)
t1 = timer()
print(t1 - t0)
# CCPi Regularisation toolkit high tolerance
r_alpha = alpha
r_iterations = iters
r_tolerance = 1e-8
r_iso = 0
r_nonneg = 0
r_printing = 0
g_CCPI_reg_toolkit = CCPiReg_FGP_TV(r_alpha, r_iterations, r_tolerance,
r_iso, r_nonneg, r_printing, 'cpu')
t2 = timer()
res2 = g_CCPI_reg_toolkit.proximal(noisy_data, 1.)
t3 = timer()
print(t3 - t2)
def test_noise_gaussian(self):
camera = dataexample.CAMERA.get()
noisy_camera = noise.gaussian(camera, seed=1)
norm = (camera - noisy_camera).norm()
self.assertAlmostEqual(norm, 48.881268, places=4)