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Changed API of nl_means_denoising function to have a multichannel flag

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emmanuelle
2015-01-25 11:49:45 +01:00
parent ec6aa2a017
commit fa13744d59
1 changed files with 9 additions and 9 deletions
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skimage/restoration/non_local_means.py
+9 -9
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@@ -5,29 +5,29 @@ from skimage.restoration._nl_means_denoising import _nl_means_denoising_2d, \
_fast_nl_means_denoising_2drgb
def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1,
fast_mode=True):
multichannel=True, fast_mode=True):
"""
Perform non-local means denoising on 2-D or 3-D grayscale images, and
2-D RGB images.
Parameters
----------
image : ndarray
input data to be denoised
image : 2D or 3D ndarray
input image to be denoised, which can be 2D or 3D, and grayscale
or RGB (for 2D images only, see ``multichannel`` parameter).
patch_size : int, optional
size of patches used for denoising
patch_distance : int, optional
maximal distance in pixels where to search patches used for denoising
h : float, optional
cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches. A higher h results in a smoother image,
at the expense of blurring features. For a Gaussian noise of standard
deviation sigma, a rule of thumb is to choose the value of h to be
sigma of slightly less.
multichannel : bool, optional
Whether the last axis of the image is to be interpreted as multiple
channels or another spatial dimension.
fast_mode : bool, optional
if True (default value), a fast version of the non-local means
algorithm is used. If False, the original version of non-local means is
@@ -105,14 +105,14 @@ def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1,
else:
return np.array(_nl_means_denoising_2d(image, s=patch_size,
d=patch_distance, h=h))
if image.ndim == 3 and image.shape[-1] > 4: # only grayscale
elif image.ndim == 3 and not multichannel: # only grayscale
if fast_mode:
return np.array(_fast_nl_means_denoising_3d(image, s=patch_size,
d=patch_distance, h=h))
else:
return np.array(_nl_means_denoising_3d(image, patch_size,
patch_distance, h))
if image.ndim == 3 and image.shape[-1] == 3: # 2-D color (RGB) images
if image.ndim == 3 and multichannel: # 2-D color (RGB) images
if fast_mode:
return np.array(_fast_nl_means_denoising_2drgb(image, patch_size,
patch_distance, h))