Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class mri.operators.linear.dictionary.DictionaryLearning(img_shape, dictionary_r, dictionary_i=None)[source]¶

This class defines the sparse encoder in a learnt dictionary (using MiniBatchDictionaryLearning from sklearn) and its back projection.

adj_op(coeffs, dtype='array')[source]¶

Adjoint operator.

This method returns the reconsructed image from the sparse coefficients.

Remark: This method only works for squared patches

Parameters

coeffs : ndarray of floats,

2d matrix dim nb_patches*nb_components, the sparse coefficients.

dtype : str, default ‘array’

if ‘array’ return the data as a ndarray, otherwise return a pysap.Image.

Returns

ndarray, the reconstructed data.

l2norm(data_shape)[source]¶

Compute the L2 norm.

op(image)[source]¶

Operator.

This method returns the representation of the input data in the learnt dictionary, that is to say the sparse coefficients.

Remark: This method only works for squared patches

Parameters: image : ndarray

Input data array, a 2D image.
Returns: coeffs : ndarray of complex if is_complex, default(float)

2d matrix dim nb_patches*nb_components, the sparse coefficients.