`skimage.transform.radon_transform:iradon`

source: /dev/scikit-image/src/skimage/transform/radon_transform.py :187

Signature

   def     iradon (    radon_image    ,    theta    =  None   ,    output_size    =  None   ,    filter_name    =  ramp   ,    interpolation    =  linear   ,    circle    =  True   ,    preserve_range    =  True     )

Summary

Inverse radon transform.

Extended Summary

Reconstruct an image from the radon transform, using the filtered back projection algorithm.

Parameters

radon_image : ndarray: Image containing radon transform (sinogram). Each column of the image corresponds to a projection along a different angle. The tomography rotation axis should lie at the pixel index radon_image.shape[0] // 2 along the 0th dimension of radon_image.
theta : array, optional: Reconstruction angles (in degrees). Default: m angles evenly spaced between 0 and 180 (if the shape of radon_image is (N, M)).
output_size : int, optional: Number of rows and columns in the reconstruction.
filter_name : str, optional: Filter used in frequency domain filtering. Ramp filter used by default. Filters available: ramp, shepp-logan, cosine, hamming, hann. Assign None to use no filter.
interpolation : str, optional: Interpolation method used in reconstruction. Methods available: 'linear', 'nearest', and 'cubic' ('cubic' is slow).
circle : bool, optional: Assume the reconstructed image is zero outside the inscribed circle. Also changes the default output_size to match the behaviour of radon called with circle=True.
preserve_range : bool, optional: Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. Also see https://scikit-image.org/docs/dev/user_guide/data_types.html

Returns

reconstructed : ndarray: Reconstructed image. The rotation axis will be located in the pixel with indices (reconstructed.shape[0] // 2, reconstructed.shape[1] // 2).
: .. versionchanged:: 0.19: In iradon, filter argument is deprecated in favor of filter_name.

Notes

It applies the Fourier slice theorem to reconstruct an image by multiplying the frequency domain of the filter with the FFT of the projection data. This algorithm is called filtered back projection.