bartorch.prox.Wavelet#

class bartorch.prox.Wavelet(axes, weight, joint_axes=(), *, family='dau2', randshift=True)#

l1 norm of the wavelet transform over axes (pics -R W).

Parameters:

axes (int or tuple of int) – Axes to transform, as indices into the image’s shape.
weight (float)
joint_axes (int or tuple of int, optional) – Axes along which a coefficient is kept or zeroed together.
family ({'haar', 'dau2', 'cdf44'}) – Wavelet family (pics --wavelet).
randshift (bool) – Cycle-spin the transform by a random shift; pics -n turns it off.

Examples

>>> Wavelet(axes=(-1, -2), weight=0.005)

__init__(axes, weight, joint_axes=(), *, family='dau2', randshift=True)#

Methods

`__init__`(axes, weight[, joint_axes, family, ...])
`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

bartorch.prox.Wavelet