nabu.reconstruction.rings

Classes

MunchDeringer — Initialize a "Munch Et Al" sinogram deringer. See References for more information.
VoDeringer — An interface to Nghia Vo's "remove_all_stripe". Needs algotom to run.
SinoMeanDeringer — Rings correction with mean subtraction/division. The principle of this method is to subtract (or divide) the sinogram by its mean along a certain axis. In short: sinogram -= filt(sinogram.mean(axis=0)) where filt is some bandpass filter.

source class MunchDeringer(sigma, sinos_shape, levels=None, wname='db15', padding=None, padding_mode='edge')

Initialize a "Munch Et Al" sinogram deringer. See References for more information.

Parameters

sigma : float — Standard deviation of the damping parameter. The higher value of sigma, the more important the filtering effect on the rings.
levels : int, optional — Number of wavelets decomposition levels. By default (None), the maximum number of decomposition levels is used.
wname : str, optional — Default is "db15" (Daubechies, 15 vanishing moments)
sinos_shape : tuple, optional — Shape of the sinogram (or sinograms stack).
padding : tuple of two int, optional — Horizontal padding to use for reducing the aliasing artefacts

References

B. Munch, P. Trtik, F. Marone, M. Stampanoni, Stripe and ring artifact removal with combined wavelet-Fourier filtering, Optics Express 17(10):8567-8591, 2009.

Methods

remove_rings — Main function to performs rings artefacts removal on sinogram(s). CAUTION: this function defaults to in-place processing, meaning that the sinogram(s) you pass will be overwritten.

source method MunchDeringer.remove_rings(sinos, output=None)

Main function to performs rings artefacts removal on sinogram(s). CAUTION: this function defaults to in-place processing, meaning that the sinogram(s) you pass will be overwritten.

Parameters

sinos : numpy.ndarray — Sinogram or stack of sinograms.
output : numpy.ndarray, optional — Output array. If set to None (default), the output overwrites the input.

source class VoDeringer(sinos_shape, **remove_all_stripe_options)

An interface to Nghia Vo's "remove_all_stripe". Needs algotom to run.

Methods

source method VoDeringer.remove_rings_sinogram(sino, output=None)

source method VoDeringer.remove_rings_sinograms(sinos, output=None)

source method VoDeringer.remove_rings_radios(radios)

source class SinoMeanDeringer(sinos_shape, mode='subtract', filter_cutoff=None, padding_mode='edge', fft_num_threads=None)

Rings correction with mean subtraction/division. The principle of this method is to subtract (or divide) the sinogram by its mean along a certain axis. In short: sinogram -= filt(sinogram.mean(axis=0)) where filt is some bandpass filter.

Parameters

sinos_shape : tuple of int — Sinograms shape, in the form (n_angles, n_x) or (n_sinos, n_angles, n_x)
mode : str, optional — Operation to do on the sinogram, either "subtract" or "divide"
filter_cutoff : tuple, optional — Cut-off of the bandpass filter applied on the sinogram profiles. Empty (default) means no filtering. Possible values forms are: - (sigma_low, sigma_high): two float values defining the standard deviation of gaussian(sigma_low) * (1 - gaussian(sigma_high)). High values of sigma mean stronger effect of associated filters. - ((cutoff_low, transition_low), (cutoff_high, transition_high)) where "cutoff" is in normalized Nyquist frequency (0.5 is the maximum frequency), and "transition" is the width of filter decay in fraction of the cutoff frequency
padding_mode : str, optional — Padding mode when filtering the sinogram profile. Should be "constant" (i.e "zeros") for mathematical correctness, but in practice this yields a Gibbs effect when replicating the sinogram, so "edges" is recommended.
fft_num_threads : int, optional — How many threads to use for computing the fast Fourier transform when filtering the sinogram profile. Defaut is all the available threads.

Methods

source method SinoMeanDeringer.remove_rings_sinogram(sino, output=None)

Raises

NotImplementedError

source method SinoMeanDeringer.remove_rings_sinograms(sinos, output=None)

Raises

NotImplementedError