bartorch.prox.TotalGeneralizedVariation#
- class bartorch.prox.TotalGeneralizedVariation(axes, weight, joint_axes=(), *, alpha=(1.0, 3.0**0.5))#
Total generalized variation over
axes(pics -R G).Adds unknowns to the optimization:
bartorch.tools.pics(),bartorch.optim.ADMMandbartorch.optim.PRIDUtake it, and nothing else does. See the module’s introduction.- Parameters:
axes (int or tuple of int) – Axes to differentiate, as indices into the image’s shape.
weight (float)
joint_axes (int or tuple of int, optional)
alpha (tuple of float) – BART’s
alpha1:alpha0pair (pics --alpha).
- __init__(axes, weight, joint_axes=(), *, alpha=(1.0, 3.0**0.5))#
Methods
__init__(axes, weight[, joint_axes, alpha])apply_transform(x[, image_shape, mode])This term's transform applied to
x, without making an operator of it.build(shape)The BART operator for this term over an image of C-order
shape.prox(x[, gamma, image_shape])prox_{gamma f}(x), the operator BART's solvers call.prox_shape(image_shape)The C-order shape this term's proximal operator works on.
rewind(image_shape)Put this term's own random generator back to where it started.
transform(image_shape)The operator BART puts in front of this term's proximal operator.
transform_is_identity(image_shape)Whether that transform is the identity, as BART decides it.
Attributes
axescountjoint_axeskindweight