nabu.preproc.phase_cuda module

class nabu.preproc.phase_cuda.CudaPaganinPhaseRetrieval(shape, distance=0.5, energy=20, delta_beta=250.0, pixel_size=1e-06, padding='edge', margin=None, cuda_options=None, fftw_num_threads=None, fft_num_threads=None, fft_backend='skcuda')[source]

Bases: PaganinPhaseRetrieval

Paganin Phase Retrieval for an infinitely distant point source. Formula (10) in [1].

Parameters:

  • shape (int or tuple) – Shape of each radio, in the format (num_rows, num_columns), i.e (size_vertical, size_horizontal). If an integer is provided, the shape is assumed to be square.

  • distance (float, optional) – Propagation distance in meters.

  • energy (float, optional) – Energy in keV.

  • delta_beta (float, optional) – delta/beta ratio, where n = (1 - delta) + i*beta is the complex refractive index of the sample.

  • pixel_size (float, optional) – Detector pixel size in meters. Default is 1e-6 (one micron)

  • padding (str, optional) – Padding method. Available are “zeros”, “mean”, “edge”, “sym”, “reflect”. Default is “edge”. Please refer to the “Padding” section below for more details.

  • margin (tuple, optional) –

    The user may provide integers values U, D, L, R as a tuple under the form ((U, D), (L, R)) (same syntax as numpy.pad()). The resulting filtered radio will have a size equal to (size_vertic - U - D, size_horiz - L - R). These values serve to create a “margin” for the filtering process, where U, D, L R are the margin of the Up, Down, Left and Right part, respectively. The filtering is done on a subset of the input radio. The subset size is (Nrows - U - D, Ncols - R - L). The margins is used to do the padding for the rest of the padded array.

    For example in one dimension, where padding="edge":

    <------------------------------ padded_size --------------------------->
[padding=edge | padding=data | radio data | padding=data | padding=edge]
<------ N2 ---><----- L -----><- (N-L-R)--><----- R -----><----- N2 --->

    Some or all the values U, D, L, R can be 0. In this case, the padding of the parts related to the zero values will fall back to the one of “padding” parameter. For example, if padding=”edge” and L, R are 0, then the left and right parts will be padded with the edges, while the Up and Down parts will be padded using the the user-provided margins of the radio, and the final data will have shape (Nrows - U - D, Ncols). Some or all the values U, D, L, R can be the string “auto”. In this case, the values of U, D, L, R are automatically computed as a function of the Paganin filter width.

  • use_rfft (bool, optional) – Whether to use Real-to-Complex (R2C) transform instead of standard Complex-to-Complex transform, providing better performances

  • use_R2C (bool, optional) – DEPRECATED, use use_rfft instead

  • fftw_num_threads (bool or None or int, optional) – DEPRECATED - please use fft_num_threads

  • fft_num_threads (bool or None or int, optional) – Number of threads for FFT. Default is to use all available threads. You can pass a negative number to use N - fft_num_threads cores.

Important

Mind the units! Distance and pixel size are in meters, and energy is in keV.

Notes

Padding methods

The phase retrieval is a convolution done in Fourier domain using FFT, so the Fourier transform size has to be at least twice the size of the original data. Mathematically, the data should be padded with zeros before being Fourier transformed. However, in practice, this can lead to artefacts at the edges (Gibbs effect) if the data does not go to zero at the edges. Apart from applying an apodization (Hamming, Blackman, etc), a common strategy to avoid these artefacts is to pad the data. In tomography reconstruction, this is usually done by replicating the last(s) value(s) of the edges ; but one can think of other methods:

  • “zeros”: the data is simply padded with zeros.

  • “mean”: the upper side of extended data is padded with the mean of the first row, the lower side with the mean of the last row, etc.

  • “edge”: the data is padded by replicating the edges. This is the default mode.

  • “sym”: the data is padded by mirroring the data with respect to its edges. See numpy.pad().

  • “reflect”: the data is padded by reflecting the data with respect to its edges, including the edges. See numpy.pad().

Formulas

The radio is divided, in the Fourier domain, by the original “Paganin filter” [1].

\[F = 1 + \frac{\delta}{\beta} \lambda D \pi |k|^2\]

where k is the wave vector.

References

[1] D. Paganin Et Al, “Simultaneous phase and amplitude extraction

from a single defocused image of a homogeneous object”, Journal of Microscopy, Vol 206, Part 1, 2002

supported_paddings = ['zeros', 'constant', 'edge']
set_input(data)[source]
get_output(output)[source]
apply_filter(radio, output=None)[source]
retrieve_phase(radio, output=None)