mri.operators.proximity.ordered_weighted_l1_norm

class OWL(alpha, beta, bands_shape, n_coils, mode='band_based', n_jobs=1)

Bases: modopt.opt.proximity.ProximityParent

This class handles reshaping coefficients based on mode and feeding in right format the OWL operation to OrderedWeightedL1Norm

Parameters

• alpha (float) – value of alpha for parameterizing weights

• beta (float) – value of beta for parameterizing weights

• band_shape (list of tuples) – the shape of all bands, this corresponds to linear_op.coeffs_shape

• n_coils (int) – number of channels

• mode (string 'all' | 'band_based' | 'coeff_based' | 'scale_based',) – default ‘band_based’ Mode of operation of proximity: all -> on all coefficients in all channels band_based -> on all coefficients in each band coeff_based -> on all coefficients but across each channel scale_based -> on al coefficients in each scale

• n_jobs (int, default 1) – number of cores to be used for operation

static _oscar_weights(alpha, beta, size)

Here we parametrize weights based on alpha and beta

_reshape_band_based(data)

Function to reshape incoming data based on bands

_reshape_scale_based(data)

_op_method(data, extra_factor=1.0)

Based on mode, reshape the coefficients and call OrderedWeightedL1Norm

Parameters

data (numpy.ndarray) – Input array of data

_cost_method(data)

Cost function Based on mode, reshape the incoming data and call cost in OrderedWeightedL1Norm This method calculate the cost function of the proximable part.

Parameters

data (numpy.ndarray) – Input array of the sparse code.

Returns

Return type

The cost of this sparse code