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update default weight to reflect the bugfix from hardcoded 0.5 to 1/(2*ndim)

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use the default weight in all the tests as well.
This commit is contained in:
Gregory R. Lee
2015-12-24 12:34:11 -05:00
parent dd5a708a33
commit e6a1a19337
2 changed files with 9 additions and 9 deletions
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skimage/restoration/_denoise.py
+2 -2
View File
@@ -116,7 +116,7 @@ def denoise_tv_bregman(image, weight, max_iter=100, eps=1e-3, isotropic=True):
return _denoise_tv_bregman(image, weight, max_iter, eps, isotropic)
def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
def _denoise_tv_chambolle_nd(im, weight=0.1, eps=2.e-4, n_iter_max=200):
"""Perform total-variation denoising on n-dimensional images.
Parameters
@@ -198,7 +198,7 @@ def _denoise_tv_chambolle_nd(im, weight=0.2, eps=2.e-4, n_iter_max=200):
return out
def denoise_tv_chambolle(im, weight=0.2, eps=2.e-4, n_iter_max=200,
def denoise_tv_chambolle(im, weight=0.1, eps=2.e-4, n_iter_max=200,
multichannel=False):
"""Perform total-variation denoising on n-dimensional images.
skimage/restoration/tests/test_denoise.py
+7 -7
View File
@@ -21,7 +21,7 @@ def test_denoise_tv_chambolle_2d():
# clip noise so that it does not exceed allowed range for float images.
img = np.clip(img, 0, 1)
# denoise
denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.25)
denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1)
# which dtype?
assert denoised_astro.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage as ndi
@@ -34,8 +34,8 @@ def test_denoise_tv_chambolle_2d():
def test_denoise_tv_chambolle_multichannel():
denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.25)
denoised = restoration.denoise_tv_chambolle(astro, weight=0.25,
denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1)
denoised = restoration.denoise_tv_chambolle(astro, weight=0.1,
multichannel=True)
assert_equal(denoised[..., 0], denoised0)
@@ -46,7 +46,7 @@ def test_denoise_tv_chambolle_float_result_range():
int_astro = np.multiply(img, 255).astype(np.uint8)
assert np.max(int_astro) > 1
denoised_int_astro = restoration.denoise_tv_chambolle(int_astro,
weight=0.25)
weight=0.1)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_astro.dtype == np.float
assert np.max(denoised_int_astro) <= 1.0
@@ -62,7 +62,7 @@ def test_denoise_tv_chambolle_3d():
mask += 20 * np.random.rand(*mask.shape)
mask[mask < 0] = 0
mask[mask > 255] = 255
res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.4)
res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < mask.std()
@@ -72,7 +72,7 @@ def test_denoise_tv_chambolle_1d():
x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000))
x += 20 * np.random.rand(x.size)
x = np.clip(x, 0, 255)
res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.5)
res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < x.std()
@@ -80,7 +80,7 @@ def test_denoise_tv_chambolle_1d():
def test_denoise_tv_chambolle_4d():
""" TV denoising for a 4D input."""
im = 255 * np.random.rand(8, 8, 8, 8)
res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.5)
res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < im.std()