data_matching = odl.solvers.L2NormSquared(ray_trafo.range).translated(data)
data_matching = data_matching * ray_trafo # compose from the right
# Define the lie group to use.
lie_grp_type = 'rigid'
if lie_grp_type == 'gln':
lie_grp = lgd.GLn(space.ndim)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'son':
lie_grp = lgd.SOn(space.ndim)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'sln':
lie_grp = lgd.SLn(space.ndim)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'affine':
lie_grp = lgd.AffineGroup(space.ndim)
deform_action = lgd.MatrixImageAffineAction(lie_grp, space)
elif lie_grp_type == 'rigid':
lie_grp = lgd.EuclideanGroup(space.ndim)
deform_action = lgd.MatrixImageAffineAction(lie_grp, space)
else:
assert False
# Define what regularizer to use
regularizer = 'point'
if regularizer == 'image':
# Create set of all points in space
W = space.tangent_bundle
w = W.element(space.points().T)
# Create regularizing functional
# Define data matching functional
data_matching = odl.solvers.L2NormSquared(space).translated(target)
# Define the lie group to use.
lie_grp_type = 'rigid'
if lie_grp_type == 'gln':
lie_grp = lgd.GLn(1)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'son':
lie_grp = lgd.SOn(1)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'sln':
lie_grp = lgd.SLn(1)
deform_action = lgd.MatrixImageAction(lie_grp, space)
elif lie_grp_type == 'affine':
lie_grp = lgd.AffineGroup(1)
deform_action = lgd.MatrixImageAffineAction(lie_grp, space)
elif lie_grp_type == 'rigid':
lie_grp = lgd.EuclideanGroup(1)
deform_action = lgd.MatrixImageAffineAction(lie_grp, space)
else:
assert False
# Define what regularizer to use
regularizer = 'image'
if regularizer == 'image':
# Create set of all points in space
W = space.tangent_bundle
w = W.element(space.points().T)
# Create regularizing functional