nabu.thirdparty.tomocupy_remove_stripe module

This file is a “GPU” (through cupy) implementation of “remove_all_stripe”. The original method is implemented by Nghia Vo in the algotom project: https://github.com/algotom/algotom/blob/master/algotom/prep/removal.py The implementation using cupy is done by Viktor Nikitin in the tomocupy project: https://github.com/tomography/tomocupy/blame/main/src/tomocupy/remove_stripe.py License follows.

For now we can’t rely on off-the-shelf tomocupy as it’s not packaged in pypi, and compilation is quite tedious.

nabu.thirdparty.tomocupy_remove_stripe.afb1d(x, h0, h1='zero', dim=-1)

1D analysis filter bank (along one dimension only) of an image

Parameters:

• (array) (h1) –

• (array) – h, 1) or (1, 1, 1, w)

• (array) – h, 1) or (1, 1, 1, w)

• (called (dim (int) - dimension of filtering. d=2 is for a vertical filter) – column filtering but filters across the rows). d=3 is for a horizontal filter, (called row filtering but filters across the columns).

Returns:

lohi – dimension

Return type:

lowpass and highpass subbands concatenated along the channel

nabu.thirdparty.tomocupy_remove_stripe.sfb1d(lo, hi, g0, g1='zero', dim=-1)

1D synthesis filter bank of an image Array

class nabu.thirdparty.tomocupy_remove_stripe.DWTForward(wave='db1')

Bases: object

Performs a 2d DWT Forward decomposition of an image

Parameters:

wave (str) – Which wavelet to use.

apply(x)

Forward pass of the DWT.

Parameters:

x (array) – Input of shape \((N, C_{in}, H_{in}, W_{in})\)

Returns:

(yl, yh)

tuple of lowpass (yl) and bandpass (yh) coefficients. yh is a list of scale coefficients. yl has shape \((N, C_{in}, H_{in}', W_{in}')\) and yh has shape \(list(N, C_{in}, 3, H_{in}'', W_{in}'')\). The new dimension in yh iterates over the LH, HL and HH coefficients.

Note

\(H_{in}', W_{in}', H_{in}'', W_{in}''\) denote the correctly downsampled shapes of the DWT pyramid.

class nabu.thirdparty.tomocupy_remove_stripe.DWTInverse(wave='db1')

Bases: object

Performs a 2d DWT Inverse reconstruction of an image

Parameters:

wave (str) – Which wavelet to use.

apply(coeffs)

Parameters:

coeffs (yl, yh) – tuple of lowpass and bandpass coefficients, where: yl is a lowpass array of shape \((N, C_{in}, H_{in}', W_{in}')\) and yh is a list of bandpass arrays of shape \(list(N, C_{in}, 3, H_{in}'', W_{in}'')\). I.e. should match the format returned by DWTForward

Returns:

Reconstructed input of shape \((N, C_{in}, H_{in}, W_{in})\)

Note

\(H_{in}', W_{in}', H_{in}'', W_{in}''\) denote the correctly downsampled shapes of the DWT pyramid.

nabu.thirdparty.tomocupy_remove_stripe.remove_stripe_fw(data, sigma, wname, level)

Remove stripes with wavelet filtering

nabu.thirdparty.tomocupy_remove_stripe.remove_stripe_ti(data, beta, mask_size)

Remove stripes with a new method by V. Titareno

nabu.thirdparty.tomocupy_remove_stripe.remove_all_stripe(tomo, snr=3, la_size=61, sm_size=21, dim=1)

Remove all types of stripe artifacts from sinogram using Nghia Vo’s approach :cite:`Vo:18` (combination of algorithm 3,4,5, and 6).

Parameters:

• tomo (ndarray) – 3D tomographic data.

• snr (float) – Ratio used to locate large stripes. Greater is less sensitive.

• la_size (int) – Window size of the median filter to remove large stripes.

• sm_size (int) – Window size of the median filter to remove small-to-medium stripes.

• dim ({1, 2}, optional) – Dimension of the window.

Returns:

Corrected 3D tomographic data.

Return type:

ndarray

nabu.thirdparty.tomocupy_remove_stripe.remove_all_stripe_pycuda(radios, device_id=0, **kwargs)

Nabu interface to “remove_all_stripe”. In-place!

Parameters:

• radios (pycuda.GPUArray) – Stack of radios in the shape (n_angles, n_y, n_x) so that sinogram number i is radios[:, i, :]

• 'remove_all_stripe (See parameters of) –