Source code for ants.utils.denoise_image

__all__ = ["denoise_image"]

from .. import utils
from . import process_args as pargs
from .get_mask import get_mask


def denoise_image(
    image, mask=None, shrink_factor=1, p=1, r=3, noise_model="Rician", v=0
):
    """
    Denoise an image using a spatially adaptive filter originally described in
    J. V. Manjon, P. Coupe, Luis Marti-Bonmati, D. L. Collins, and M. Robles.
    Adaptive Non-Local Means Denoising of MR Images With Spatially Varying
    Noise Levels, Journal of Magnetic Resonance Imaging, 31:192-203, June 2010.

    ANTsR function: `denoiseImage`

    Arguments
    ---------
    image : ANTsImage
        scalar image to denoise.

    mask : ANTsImage
        to limit the denoise region.

    shrink_factor : scalar
        downsampling level performed within the algorithm.

    p : integer or character of format '2x2' where the x separates vector entries
        patch radius for local sample.

    r : integer or character of format '2x2' where the x separates vector entries
        search radius from which to choose extra local samples.

    noise_model : string
        'Rician' or 'Gaussian'

    Returns
    -------
    ANTsImage

    Example
    -------
    >>> import ants
    >>> import numpy as np
    >>> image = ants.image_read(ants.get_ants_data('r16'))
    >>> # add fairly large salt and pepper noise
    >>> imagenoise = image + np.random.randn(*image.shape).astype('float32')*5
    >>> imagedenoise = ants.denoise_image(imagenoise, ants.get_mask(image))
    """
    inpixeltype = image.pixeltype
    outimage = image.clone("float")

    mydim = image.dimension

    if mask is None:
        myargs = {
            "d": mydim,
            "i": image,
            "n": noise_model,
            "s": int(shrink_factor),
            "p": p,
            "r": r,
            "o": outimage,
            "v": v,
        }
    else:
        myargs = {
            "d": mydim,
            "i": image,
            "n": noise_model,
            "x": mask.clone("unsigned char"),
            "s": int(shrink_factor),
            "p": p,
            "r": r,
            "o": outimage,
            "v": v,
        }

    processed_args = pargs._int_antsProcessArguments(myargs)
    libfn = utils.get_lib_fn("DenoiseImage")
    libfn(processed_args)
    return outimage.clone(inpixeltype)