odl.Operator.__init__(self, space, space, True)
def _call(self, x):
result = self.range.zero()
for k in range(self.domain.size):
for i in range(self.domain.size):
for j in range(self.domain[0].size):
result[i][j] += self.arr[k][i] * x[k][j]
return result
@property
def adjoint(self):
return LamOp(self.domain, self.arr.T)
data, geometry, crlb = load_fan_data(return_crlb=True)
space = odl.uniform_discr([-129, -129], [129, 129], [400, 400])
ray_trafo = odl.tomo.RayTransform(space, geometry, impl='astra_cuda')
A = odl.DiagonalOperator(ray_trafo, 2)
grad = odl.Gradient(space)
grad_vec = odl.DiagonalOperator(grad, 2)
cross_terms = True
c = 0.5
if not cross_terms:
crlb[1, 0, ...] = 0
crlb[0, 1, ...] = 0
"""Example of reconstruction with the Huber norm.
Here, the huber norm is implemented using the moreau envelope, for convenience.
The problem is solved using the BFGS quasi-newton method.
"""
import odl
from util import load_data, load_fan_data
material = 'water'
lam = 4
sigma = 0.005
data, geometry = load_fan_data()
space = odl.uniform_discr([-129, -129], [129, 129], [400, 400])
ray_trafo = odl.tomo.RayTransform(space, geometry, impl='astra_cuda')
# Data discrepancy
if material == 'water':
b = ray_trafo.range.element(data[0])
elif material == 'bone':
b = ray_trafo.range.element(data[1])
l2 = odl.solvers.L2NormSquared(ray_trafo.range) * (ray_trafo - b)
# Create huber norm
grad = odl.Gradient(space)
l1_norm = odl.solvers.GroupL1Norm(grad.range)