def test_FISTA_Denoising(self):
if debug_print:
print("FISTA Denoising Poisson Noise Tikhonov")
# adapted from demo FISTA_Tikhonov_Poisson_Denoising.py in CIL-Demos repository
data = dataexample.SHAPES.get()
ig = data.geometry
ag = ig
N = 300
# Create Noisy data with Poisson noise
scale = 5
noisy_data = applynoise.poisson(data / scale, seed=10) * scale
# Regularisation Parameter
alpha = 10
# Setup and run the FISTA algorithm
operator = GradientOperator(ig)
fid = KullbackLeibler(b=noisy_data)
reg = OperatorCompositionFunction(alpha * L2NormSquared(), operator)
initial = ig.allocate()
fista = FISTA(initial=initial, f=reg, g=fid)
fista.max_iteration = 3000
fista.update_objective_interval = 500
fista.run(verbose=0)
rmse = (fista.get_output() - data).norm() / data.as_array().size
if debug_print:
print("RMSE", rmse)
self.assertLess(rmse, 4.2e-4)
def test_FISTA_Norm2Sq(self):
print("Test FISTA Norm2Sq")
ig = ImageGeometry(127, 139, 149)
b = ig.allocate(ImageGeometry.RANDOM)
# fill with random numbers
initial = ig.allocate(ImageGeometry.RANDOM)
identity = IdentityOperator(ig)
#### it seems FISTA does not work with Nowm2Sq
norm2sq = LeastSquares(identity, b)
#norm2sq.L = 2 * norm2sq.c * identity.norm()**2
#norm2sq = OperatorCompositionFunction(L2NormSquared(b=b), identity)
opt = {'tol': 1e-4, 'memopt': False}
if debug_print:
print("initial objective", norm2sq(initial))
alg = FISTA(initial=initial, f=norm2sq, g=ZeroFunction())
alg.max_iteration = 2
alg.run(20, verbose=0)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
alg = FISTA(initial=initial,
f=norm2sq,
g=ZeroFunction(),
max_iteration=2,
update_objective_interval=3)
self.assertTrue(alg.max_iteration == 2)
self.assertTrue(alg.update_objective_interval == 3)
alg.run(20, verbose=0)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())