bartorch.prox.WaveletNIHT

class bartorch.prox.WaveletNIHT(axes, count, joint_axes=(), *, family='dau2', randshift=True)

Keep the count largest wavelet coefficients over axes (pics -R H).

Parameters:

• axes (int or tuple of int) – Axes to transform, as indices into the image’s shape.

• count (int)

• 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.

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

Methods

__init__(axes, count[, 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

axes
count
joint_axes
kind
weight