Menu

Python Sparse data Analysis Package external MRI plugin.

This module contains linears operators classes.

class mri.operators.linear.wavelet.WaveletN(wavelet_name, nb_scale=4, verbose=0, dim=2, n_coils=1, n_jobs=1, backend='threading', **kwargs)[source]

The 2D and 3D wavelet transform class.

adj_op(coefs)[source]

Define the wavelet adjoint operator. This method returns the reconstructed image.

Parameters

coeffs: ndarray

the wavelet coefficients.

Returns

data: ndarray

the reconstructed data.

l2norm(shape)[source]

Compute the L2 norm.

Parameters

shape: uplet

the data shape.

Returns

norm: float

the L2 norm.

op(data)[source]

Define the wavelet operator. This method returns the input data convolved with the wavelet filter.

Parameters

data: ndarray or Image

input 2D data array.

Returns

coeffs: ndarray

the wavelet coefficients.

class mri.operators.linear.wavelet.WaveletUD2(wavelet_id=24, nb_scale=4, n_jobs=1, backend='threading', n_coils=1, verbose=0)[source]

The wavelet undecimated operator using pysap wrapper.

adj_op(coefs)[source]

Define the wavelet adjoint operator. This method returns the reconstructed image.

Parameters

coeffs: ndarray

the wavelet coefficients.

Returns

data: ndarray

the reconstructed data.

l2norm(shape)[source]

Compute the L2 norm.

Parameters

shape: uplet

the data shape.

Returns

norm: float

the L2 norm.

op(data)[source]

Define the wavelet operator. This method returns the input data convolved with the wavelet filter.

Parameters

data: ndarray or Image

input 2D data array.

Returns

coeffs: ndarray

the wavelet coefficients.

Follow us

© 2019, Antoine Grigis Samuel Farrens Jean-Luc Starck Philippe Ciuciu .
Inspired by AZMIND template.