tomocupy.remove_stripe

Functions:

DWTForward([wave])

Performs a 2d DWT Forward decomposition of an image

DWTInverse([wave])

Performs a 2d DWT Inverse reconstruction of an image

afb1d(x, h0[, h1, dim])

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

remove_all_stripe(tomo[, snr, la_size, ...])

Remove all types of stripe artifacts from sinogram using Nghia Vo's approach [B3] (combination of algorithm 3,4,5, and 6).

remove_stripe_fw(data, sigma, wname, level)

Remove stripes with wavelet filtering

remove_stripe_ti(data, beta, mask_size)

Remove stripes with a new method by V.
class 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 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.

tomocupy.remove_stripe.afb1d(x, h0, h1='zero', dim=-1)

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

Parameters

  • x (array) (4D input with the last two dimensions the spatial input)

  • h0 (array) (4D input for the lowpass filter. Should have shape (1, 1,) – h, 1) or (1, 1, 1, w)

  • h1 (array) (4D input for the highpass filter. Should have shape (1, 1,) – h, 1) or (1, 1, 1, w)

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

Returns

lohi (lowpass and highpass subbands concatenated along the channel) – dimension

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 [B3] (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

ndarray – Corrected 3D tomographic data.

tomocupy.remove_stripe.remove_stripe_fw(data, sigma, wname, level)

Remove stripes with wavelet filtering

tomocupy.remove_stripe.remove_stripe_ti(data, beta, mask_size)

Remove stripes with a new method by V. Titareno