def nlmeans(arr,
sigma,
mask=None,
patch_radius=1,
block_radius=5,
rician=True,
num_threads=None):
""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float or 3D array
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
num_threads : int
Number of threads. If None (default) then all available threads
will be used (all CPU cores).
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
"""
if arr.ndim == 3:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
return nlmeans_3d(arr, mask, sigma, patch_radius, block_radius,
rician).astype(arr.dtype)
elif arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
if isinstance(sigma, np.ndarray) and sigma.ndim == 3:
sigma = (np.ones(arr.shape, dtype=np.float64) *
sigma[..., np.newaxis])
else:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
for i in range(arr.shape[-1]):
denoised_arr[...,
i] = nlmeans_3d(arr[..., i], mask, sigma[..., i],
patch_radius, block_radius, rician,
num_threads).astype(arr.dtype)
return denoised_arr
else:
raise ValueError("Only 3D or 4D array are supported!", arr.shape)
def nlmeans(arr, sigma, mask=None, patch_radius=1, block_radius=5,
rician=True, num_threads=None):
""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float or 3D array
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
num_threads : int
Number of threads. If None (default) then all available threads
will be used (all CPU cores).
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
"""
if arr.ndim == 3:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
return nlmeans_3d(arr, mask, sigma,
patch_radius, block_radius,
rician, num_threads).astype(arr.dtype)
elif arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
if isinstance(sigma, np.ndarray) and sigma.ndim == 3:
sigma = (np.ones(arr.shape, dtype=np.float64) *
sigma[..., np.newaxis])
else:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
for i in range(arr.shape[-1]):
denoised_arr[..., i] = nlmeans_3d(arr[..., i],
mask,
sigma[..., i],
patch_radius,
block_radius,
rician,
num_threads).astype(arr.dtype)
return denoised_arr
else:
raise ValueError("Only 3D or 4D array are supported!", arr.shape)
def nlmeans(arr,
sigma,
mask=None,
patch_radius=1,
block_radius=5,
rician=True):
""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
"""
if arr.ndim == 3:
return nlmeans_3d(arr, mask, sigma, patch_radius, block_radius,
rician).astype(arr.dtype)
if arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
sigma_arr = np.ones(arr.shape[-1], dtype=np.float32) * sigma
for i in range(arr.shape[-1]):
sigma = sigma_arr[i]
denoised_arr[..., i] = nlmeans_3d(arr[..., i], mask, sigma,
patch_radius, block_radius,
rician).astype(arr.dtype)
return denoised_arr
def nlmeans(arr, sigma, mask=None, patch_radius=1, block_radius=5, rician=True):
""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
"""
if arr.ndim == 3:
return nlmeans_3d(arr, mask, sigma,
patch_radius, block_radius,
rician).astype(arr.dtype)
if arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
sigma_arr = np.ones(arr.shape[-1], dtype=np.float32) * sigma
for i in range(arr.shape[-1]):
sigma = sigma_arr[i]
denoised_arr[..., i] = nlmeans_3d(arr[..., i],
mask,
sigma,
patch_radius,
block_radius,
rician).astype(arr.dtype)
return denoised_arr
def nlmeans(arr, sigma, mask=None, patch_radius=1, block_radius=5,
rician=True, num_threads=None):
r""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float or 3D array
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
num_threads : int
Number of threads. If None (default) then all available threads
will be used (all CPU cores).
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
References
----------
.. [Descoteaux08] Descoteaux, Maxim and Wiest-Daessle`, Nicolas and Prima,
Sylvain and Barillot, Christian and Deriche, Rachid
Impact of Rician Adapted Non-Local Means Filtering on
HARDI, MICCAI 2008
"""
# warn(DeprecationWarning("function 'dipy.denoise.nlmeans'"
# " is deprecated, use module "
# "'dipy.denoise.non_local_means'"
# " instead"))
if arr.ndim == 3:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
return nlmeans_3d(arr, mask, sigma,
patch_radius, block_radius,
rician, num_threads).astype(arr.dtype)
elif arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
if isinstance(sigma, np.ndarray) and sigma.ndim == 3:
sigma = (np.ones(arr.shape, dtype=np.float64) *
sigma[..., np.newaxis])
else:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
for i in range(arr.shape[-1]):
denoised_arr[..., i] = nlmeans_3d(arr[..., i],
mask,
sigma[..., i],
patch_radius,
block_radius,
rician,
num_threads).astype(arr.dtype)
return denoised_arr
else:
raise ValueError("Only 3D or 4D array are supported!", arr.shape)