Simoncelli#

class zrad.filtering.wavelet.Simoncelli(padding_type, decomposition_level, dimensionality='3D', riesz_order=None)[source]#

IBSI non-separable Simoncelli band-pass wavelet.

The wavelet is evaluated directly in the Fourier domain. decomposition_level selects the B map; level one is the highest-frequency band. An optional Riesz multi-index applies the normalized higher-order Riesz transform to the B map.

Parameters:

  • padding_type ({"nearest", "wrap", "periodic"}) – Boundary handling. periodic is an alias for wrap.

  • decomposition_level (int) – One-based scale level of the B map.

  • dimensionality ({"2D", "3D"}) – In 2D mode each slice is filtered independently.

  • riesz_order (tuple of int, optional) – Non-negative Riesz multi-index in physical (x, y) or (x, y, z) axis order, for example (0, 2) or (0, 2, 0). Its length must match dimensionality. Omitting it or supplying an all-zero index returns the isotropic B map.

Methods

apply(image)

Apply the filter to an image or set RoiData.filtered_image.

get_params()

Return filter parameters mapped to their configured values.
Simoncelli.apply(image)#

Apply the filter to an image or set RoiData.filtered_image.

Parameters:

image (Image or RoiData) – Input image to filter. If RoiData is supplied, filtering is applied to image.image and the result is stored as filtered_image in the returned ROI data. Existing intensity, texture, and IVH prepared fields are cleared.

Returns:

filtered – Filtered image, or ROI data with filtered_image updated.

Return type:

Image or RoiData

Simoncelli.get_params()#

Return filter parameters mapped to their configured values.

Returns:

params – Constructor parameters stored by the filter instance.

Return type:

dict