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[ENH] Removed eps parameter that was not needed.

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Added a test for the case when no denoising at all is performed when h is
very small.

Corrected a bug in looping range in the classical algorithm.
This commit is contained in:
emmanuelle
2015-01-26 22:06:38 +01:00
parent 2e0b37b11d
commit 4697d66c85
2 changed files with 27 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/restoration/_nl_means_denoising.pyx
+10 -13
View File
@@ -6,8 +6,6 @@ from libc.math cimport exp
ctypedef np.float32_t DTYPE_t
cdef eps = 1.e-8
@cython.boundscheck(False)
cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
@@ -44,15 +42,15 @@ cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
cdef float tmp_diff = p1[center, center] - p2[center, center]
cdef float init = w[center, center] * tmp_diff * tmp_diff
if init > 1:
return eps
return 0.
cdef float distance = 0
for i in range(s):
# exp of large negative numbers will be 0, so we'd better stop
if distance > 5:
return eps
return 0.
for j in range(s):
tmp_diff = p1[i, j] - p2[i, j]
distance += w[i, j] * tmp_diff * tmp_diff
distance += (w[i, j] * tmp_diff * tmp_diff)
distance = exp(- distance)
return distance
@@ -94,7 +92,7 @@ cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
for i in range(s):
# exp of large negative numbers will be 0, so we'd better stop
if distance > 5:
return eps
return 0.
for j in range(s):
for color in range(3):
tmp_diff = p1[i, j, color] - p2[i, j, color]
@@ -138,7 +136,7 @@ cdef inline float patch_distance_3d(DTYPE_t [:, :, :] p1,
for i in range(s):
# exp of large negative numbers will be 0, so we'd better stop
if distance > 5:
return eps
return 0.
for j in range(s):
for k in range(s):
tmp_diff = p1[i, j, k] - p2[i, j, k]
@@ -199,11 +197,11 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
x_col_end = x_col + offset + 1
# Coordinates of test pixel and patch bounds
for i in range(max(- d, offset - x_row),
min(d + 1, n_row - x_row - 1)):
min(d + 1, n_row + offset - x_row)):
x_row_start_i = x_row_start + i
x_row_end_i = x_row_end + i
for j in range(max(- d, offset - x_col),
min(d + 1, n_col - x_col - 1)):
min(d + 1, n_col + offset - x_col)):
x_col_start_j = x_col_start + j
x_col_end_j = x_col_end + j
if n_ch == 1:
@@ -213,7 +211,6 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
padded[x_row_start_i: x_row_end_i,
x_col_start_j: x_col_end_j, 0],
w, s)
else:
weight = patch_distance_2drgb(
padded[x_row_start: x_row_end,
@@ -288,15 +285,15 @@ def _nl_means_denoising_3d(image, int s=7,
weight_sum = 0
# Coordinates of test pixel and patch bounds
for i in range(max(- d, offset - x_pln),
min(d + 1, n_pln - x_pln - 1)):
min(d + 1, n_pln + offset - x_pln)):
x_pln_start_i = x_pln_start + i
x_pln_end_i = x_pln_end + i
for j in range(max(- d, offset - x_row),
min(d + 1, n_row - x_row - 1)):
min(d + 1, n_row + offset - x_row)):
x_row_start_j = x_row_start + j
x_row_end_j = x_row_end + j
for k in range(max(- d, offset - x_col),
min(d + 1, n_col - x_col - 1)):
min(d + 1, n_col + offset - x_col)):
x_col_start_k = x_col_start + k
x_col_end_k = x_col_end + k
weight = patch_distance_3d(
skimage/restoration/tests/test_denoise.py
+17 -6
View File
@@ -148,10 +148,10 @@ def test_nl_means_denoising_2d():
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
img += 0.3*np.random.randn(*img.shape)
denoised = restoration.nl_means_denoising(img, 7, 5, 0.1, fast_mode=True)
denoised = restoration.nl_means_denoising(img, 7, 5, 0.2, fast_mode=True)
# make sure noise is reduced
assert img.std() > denoised.std()
denoised = restoration.nl_means_denoising(img, 7, 5, 0.1, fast_mode=False)
denoised = restoration.nl_means_denoising(img, 7, 5, 0.2, fast_mode=False)
# make sure noise is reduced
assert img.std() > denoised.std()
@@ -162,10 +162,10 @@ def test_nl_means_denoising_2drgb():
# add some random noise
img += 0.5 * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
denoised = restoration.nl_means_denoising(img, 7, 9, 0.08, fast_mode=True)
denoised = restoration.nl_means_denoising(img, 7, 9, 0.3, fast_mode=True)
# make sure noise is reduced
assert img.std() > denoised.std()
denoised = restoration.nl_means_denoising(img, 7, 9, 0.08, fast_mode=False)
denoised = restoration.nl_means_denoising(img, 7, 9, 0.3, fast_mode=False)
# make sure noise is reduced
assert img.std() > denoised.std()
@@ -174,11 +174,11 @@ def test_nl_means_denoising_3d():
img = np.zeros((20, 20, 10))
img[5:-5, 5:-5, 3:-3] = 1.
img += 0.3*np.random.randn(*img.shape)
denoised = restoration.nl_means_denoising(img, 5, 4, 0.1, fast_mode=True,
denoised = restoration.nl_means_denoising(img, 5, 4, 0.2, fast_mode=True,
multichannel=False)
# make sure noise is reduced
assert img.std() > denoised.std()
denoised = restoration.nl_means_denoising(img, 5, 4, 0.1, fast_mode=False,
denoised = restoration.nl_means_denoising(img, 5, 4, 0.2, fast_mode=False,
multichannel=False)
# make sure noise is reduced
assert img.std() > denoised.std()
@@ -204,5 +204,16 @@ def test_nl_means_denoising_wrong_dimension():
assert_raises(NotImplementedError, restoration.nl_means_denoising, img)
def test_no_denoising_for_small_h():
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
img += 0.3*np.random.randn(*img.shape)
# very small h should result in no averaging with other patches
denoised = restoration.nl_means_denoising(img, 7, 5, 0.01, fast_mode=True)
assert np.allclose(denoised, img)
denoised = restoration.nl_means_denoising(img, 7, 5, 0.01, fast_mode=False)
assert np.allclose(denoised, img)
if __name__ == "__main__":
run_module_suite()