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deepmr.fwt

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deepmr.fwt#

deepmr.fwt(input, ndim=None, device=None, wave_name='db4', level=None)[source]#

Forward wavelet transform.

Adapted from Sigpy [1].

Parameters:

  • input (np.ndarray | torch.Tensor) – Input signal of shape (…, nz, ny, nx).

  • ndim (int, optional) – Number of spatial dimensions over to which compute wavelet transform (1, 2, 3). Assume spatial axis are the rightmost ones. The default is None (ndim = min(3, len(input.shape))).

  • device (str, optional) – Computational device for Wavelet transform. If not specified, use input.device. The default is None.

  • wave_name (str, optional) – Wavelet name. The default is "db4".

  • axes (Iterable[int], optional) – Axes to perform wavelet transform. The default is None (all axes).

  • level (int, optional) – Number of wavelet levels. The default is None.

Returns:

  • output (np.ndarray | torch.Tensor) – Output wavelet decomposition.

  • shape (Iterable[int]) – Input signal shape (input.shape) for synthesis.

Examples

>>> import torch
>>> import deepmr

First, generate a 2D phantom and add some noise:

>>> img = deepmr.shepp_logan(128) + 0.05 * torch.randn(128, 128)

Now, run wavelet decomposition:

>>> coeff, shape = deepmr.fwt(img)

The function returns a coeff tuple, containing the Wavelet coefficients, and a shape tuple, containing the original image shape for image synthesis via deepmr.iwt:

>>> shape
torch.Size([128, 128])

References

[1] mikgroup/sigpy

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