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Switch _denoise_tv_chambolle to use C-ordering

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This commit is contained in:
Gregory R. Lee
2016-01-23 10:59:33 -05:00
parent 4a8a2189f2
commit 9f419024ad
1 changed files with 12 additions and 12 deletions
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skimage/restoration/_denoise.py
+12 -12
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@@ -117,12 +117,12 @@ def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True):
def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
"""Perform total-variation denoising on 3D images.
"""Perform total-variation denoising on n-dimensional images.
Parameters
----------
im : ndarray
3-D input data to be denoised.
n-D input data to be denoised.
weight : float, optional
Denoising weight. The greater `weight`, the more denoising (at
the expense of fidelity to `input`).
@@ -147,22 +147,22 @@ def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
"""
ndim = im.ndim
p = np.zeros(im.shape + (im.ndim, ), dtype=im.dtype, order='F')
p = np.zeros((im.ndim, ) + im.shape, dtype=im.dtype)
g = np.zeros_like(p)
d = np.zeros_like(im)
i = 0
while i < n_iter_max:
if i > 0:
d = -p.sum(-1)
d = -p.sum(0)
slices_d = [slice(None), ] * ndim
slices_p = [slice(None), ] * (ndim + 1)
for ax in range(ndim):
slices_d[ax] = slice(1, None)
slices_p[ax] = slice(0, -1)
slices_p[-1] = ax
slices_p[ax+1] = slice(0, -1)
slices_p[0] = ax
d[slices_d] += p[slices_p]
slices_d[ax] = slice(None)
slices_p[ax] = slice(None)
slices_p[ax+1] = slice(None)
out = im + d
else:
out = im
@@ -170,12 +170,12 @@ def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
slices_g = [slice(None), ] * (ndim + 1)
for ax in range(ndim):
slices_g[ax] = slice(0, -1)
slices_g[-1] = ax
slices_g[ax+1] = slice(0, -1)
slices_g[0] = ax
g[slices_g] = np.diff(out, axis=ax)
slices_g[ax] = slice(None)
slices_g[ax+1] = slice(None)
norm = np.sqrt((g*g).sum(-1))[..., np.newaxis]
norm = np.sqrt((g ** 2).sum(0))[np.newaxis, ...]
E += weight * norm.sum()
norm *= 0.5 / weight
norm += 1.
@@ -196,7 +196,7 @@ def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
def denoise_tv_chambolle(im, weight=0.2, eps=2.e-4, n_iter_max=200,
multichannel=False):
"""Perform total-variation denoising on 2D and 3D images.
"""Perform total-variation denoising on n-dimensional images.
Parameters
----------