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Added isotropic TV denoising + comment

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Ferdinand Deger
2013-07-13 09:52:09 +02:00
parent f11d5a1c8d
commit 71c70ccd71
1 changed files with 24 additions and 4 deletions
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skimage/filter/_denoise_cy.pyx
+24 -4
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@@ -200,7 +200,7 @@ def denoise_bilateral(image, Py_ssize_t win_size=5, sigma_range=None,
return np.squeeze(out)
def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3):
def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3, anisotropic=False):
"""Perform total-variation denoising using split-Bregman optimization.
Total-variation denoising (also know as total-variation regularization)
@@ -225,6 +225,8 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3):
max_iter: int, optional
Maximal number of iterations used for the optimization.
anisotropic: boolean, optimal - switch between isotropic and anisotropic tv denoising
Returns
-------
u : ndarray
@@ -239,6 +241,7 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3):
.. [3] Pascal Getreuer, "Rudin–Osher–Fatemi Total Variation Denoising
using Split Bregman" in Image Processing On Line on 2012–05–19,
http://www.ipol.im/pub/art/2012/g-tvd/article_lr.pdf
[4] http://www.math.ucsb.edu/~cgarcia/UGProjects/BregmanAlgorithms_JacquelineBush.pdf
"""
@@ -325,9 +328,26 @@ def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3):
bxx = bx[r, c, k]
byy = by[r, c, k]
s = sqrt((ux + bxx)**2 + (uy + byy)**2)
dxx = s * lam * (ux + bxx) / (s * lam + 1)
dyy = s * lam * (uy + byy) / (s * lam + 1)
# d_subproblem after reference [4]
if anisotropic:
s = ux + bxx
if s > 1/lam:
dxx = s - 1/lam
elif s < -1/lam:
dxx = s + 1/lam
else:
dxx = 0
s = uy + byy
if s > 1/lam:
dyy = s - 1/lam
elif s < -1/lam:
dyy = s + 1/lam
else:
dyy = 0
else:
s = sqrt((ux + bxx)**2 + (uy + byy)**2)
dxx = s * lam * (ux + bxx) / (s * lam + 1)
dyy = s * lam * (uy + byy) / (s * lam + 1)
dx[r, c, k] = dxx
dy[r, c, k] = dyy