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Implemented asymmetric distance computation to save speed factor of 2

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emmanuelle
2015-01-10 16:40:43 +01:00
parent 1d8e02bf38
commit ecaa959195
2 changed files with 18 additions and 15 deletions
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skimage/restoration/_nl_means_denoising.pyx
+17 -14
View File
@@ -314,7 +314,7 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
Parameters
----------
image: ndarray
input data to be denoised
2-D input data to be denoised
s: int, optional
size of patches used for denoising
@@ -326,11 +326,12 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches.
"""
if s % 2 == 0:
s += 1 # odd value for symmetric patch
cdef int offset = s / 2
cdef int pad_size = offset + d
# Image padding: we need to account for patch size, possible shift,
# + 1 for the boundary effects in finite differences
cdef int pad_size = offset + d + 1
cdef DTYPE_t [:, ::1] padded = np.ascontiguousarray(util.pad(image,
pad_size, mode='reflect').astype(np.float32))
cdef DTYPE_t [:, ::1] result = np.zeros_like(padded)
@@ -338,21 +339,23 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef DTYPE_t [:, ::1] integral = np.zeros_like(padded)
cdef int n_x, n_y, t1, t2, x, y
cdef float weight, distance
cdef float alpha
cdef float h2 = h ** 2.
cdef float s2 = s ** 2.
n_x, n_y = image.shape
n_x += 2 * pad_size
n_y += 2 * pad_size
# Outer loops on patch shifts
# With t2 >= 0, reference patch is always on the left of test patch
for t1 in range(-d, d + 1):
for t2 in range(-d, d + 1):
for t2 in range(0, d + 1):
# alpha is to account for patches on the same column
# distance is computed twice in this case
if t2 == 0 and t1 is not 0:
alpha = 0.5
else:
alpha = 1.
integral = np.zeros_like(padded)
for x in range(max(1, - t1), min(n_x, n_x - t1)):
integral[x, 0] = (padded[x, 0] - padded[x + t1, t2])**2 + \
integral[x - 1, 0]
for y in range(max(1, - t2), min(n_y, n_y - t2)):
integral[0, y] = (padded[0, y] - padded[t1, y + t2])**2 + \
integral[0, y - 1]
for x in range(max(1, - t1), min(n_x, n_x - t1)):
for y in range(max(1, - t2), min(n_y, n_y - t2)):
integral[x, y] = (padded[x, y] -
@@ -370,14 +373,14 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
distance /= (s2 * h2)
if distance > 4:
continue
weight = exp(- distance)
weight = alpha * exp(- distance)
weights[x, y] += weight
weights[x + t1, y + t2] += weight
result[x, y] += weight * padded[x + t1, y + t2]
result[x + t1, y + t2] += weight * padded[x, y]
for x in range(offset, n_x - offset):
for y in range(offset, n_x - offset):
for y in range(offset, n_y - offset):
# I think there is no risk of division by zero
# except in padded zone
result[x, y] /= weights[x, y]
return result[pad_size: - pad_size, pad_size: - pad_size]
skimage/restoration/tests/test_denoise.py
+1 -1
View File
@@ -154,7 +154,7 @@ def test_nl_means_denoising_2d():
def test_fast_nl_means_denoising_2d():
img = np.zeros((40, 40))
img = np.zeros((40, 50))
img[10:-10, 10:-10] = 1.
img += 0.3*np.random.randn(*img.shape)
denoised = restoration.fast_nl_means_denoising(img, 7, 5, 0.1)