__all__ = ["denoise_image"]
from ants.decorators import image_method
from ants.internal import get_lib_fn, process_arguments
@image_method
def denoise_image(image, mask=None, shrink_factor=1, p=1, r=2, 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 = process_arguments(myargs)
libfn = get_lib_fn("DenoiseImage")
libfn(processed_args)
return outimage.clone(inpixeltype)
