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Removed normalization of h parameters because float images will be

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normalized anyway.
This commit is contained in:
emmanuelle
2015-01-10 12:15:26 +01:00
parent 4004f048ef
commit 1d8e02bf38
2 changed files with 15 additions and 17 deletions
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skimage/restoration/_nl_means_denoising.pyx
+11 -13
View File
@@ -102,8 +102,6 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef DTYPE_t [:, ::1] padded = np.ascontiguousarray(util.pad(image,
offset, mode='reflect').astype(np.float32))
cdef DTYPE_t [:, ::1] result = padded.copy()
# We normalize by the image contrast, and divide by 3 because of 3 channels
h *= (np.max(padded) - np.min(padded)) / 3.
cdef float A = ((s - 1.) / 4.)
cdef float new_value
cdef float weight_sum, weight
@@ -115,7 +113,7 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef int x, y, i, j
cdef int x_start, x_end, y_start, y_end
cdef int x_start_i, x_end_i, y_start_j, y_end_j
w = 1. / (np.sum(w) * 2 * h ** 2) * w
w = 1. / (np.sum(w) * h ** 2.) * w
# Coordinates of central pixel and patch bounds
for x in range(offset, n_x + offset):
x_start = x - offset
@@ -180,7 +178,6 @@ def _nl_means_denoising_2drgb(image, int s=7, int d=13, float h=0.1):
((offset, offset), (offset, offset), (0, 0)),
mode='reflect').astype(np.float32))
cdef DTYPE_t [:, :, ::1] result = padded.copy()
h *= (np.max(padded) - np.min(padded))
cdef float A = ((s - 1.) / 4.)
cdef float new_value
cdef float weight_sum, weight
@@ -189,7 +186,7 @@ def _nl_means_denoising_2drgb(image, int s=7, int d=13, float h=0.1):
- (xg ** 2 + yg ** 2) / (2 * A ** 2)).
astype(np.float32))
cdef float distance
w = 1. / (np.sum(w) * 2 * h ** 2) * w
w = 1. / (np.sum(w) * h ** 2) * w
# Coordinates of central pixel and patch bounds
for x in range(offset, n_x + offset):
x_start = x - offset
@@ -255,7 +252,6 @@ def _nl_means_denoising_3d(image, int s=7,
image.astype(np.float32),
offset, mode='reflect'))
cdef DTYPE_t [:, :, ::1] result = padded.copy()
h *= (np.max(padded) - np.min(padded))
cdef float A = ((s - 1.) / 4.)
cdef float new_value
cdef float weight_sum, weight
@@ -268,7 +264,7 @@ def _nl_means_denoising_3d(image, int s=7,
cdef int x, y, z, i, j, k
cdef int x_start, x_end, y_start, y_end, z_start, z_end
cdef int x_start_i, x_end_i, y_start_j, y_end_j, z_start_k, z_end_k
w = 1. / (np.sum(w) * 2 * h ** 2) * w
w = 1. / (np.sum(w) * h ** 2) * w
# Coordinates of central pixel and patch bounds
for x in range(offset, n_x + offset):
x_start = x - offset
@@ -337,17 +333,17 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef int pad_size = offset + d
cdef DTYPE_t [:, ::1] padded = np.ascontiguousarray(util.pad(image,
pad_size, mode='reflect').astype(np.float32))
cdef DTYPE_t [:, ::1] result = padded.copy()
cdef DTYPE_t [:, ::1] result = np.zeros_like(padded)
cdef DTYPE_t [:, ::1] weights = np.zeros_like(padded)
cdef DTYPE_t [:, ::1] integral = np.zeros_like(padded)
h *= (np.max(padded) - np.min(padded))
cdef int n_x, n_y, t1, t2, x, y
cdef float weight, distance
cdef float h2 = h**2
cdef float s2 = s**2.
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
for t1 in range(-d, d + 1):
for t2 in range(-d, d + 1):
integral = np.zeros_like(padded)
@@ -371,8 +367,10 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
integral[x - offset, y - offset] - \
integral[x - offset, y + offset] - \
integral[x + offset, y - offset]
distance /= s2
weight = exp(- distance / h2)
distance /= (s2 * h2)
if distance > 4:
continue
weight = exp(- distance)
weights[x, y] += weight
result[x, y] += weight * padded[x + t1, y + t2]
for x in range(offset, n_x - offset):
skimage/restoration/nl_means_denoising.py
+4 -4
View File
@@ -66,8 +66,8 @@ def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1):
>>> denoised_a = nl_means_denoising(a, 7, 5, 0.1)
"""
if image.ndim == 2:
return np.array(_nl_means_denoising_2d(image, patch_size,
patch_distance, h))
return np.array(_nl_means_denoising_2d(image, s=patch_size,
d=patch_distance, h=h))
if image.ndim == 3 and image.shape[-1] > 4: # only grayscale
return np.array(_nl_means_denoising_3d(image, patch_size,
patch_distance, h))
@@ -150,8 +150,8 @@ def fast_nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1):
>>> denoised_a = fast_nl_means_denoising(a, 7, 5, 0.1)
"""
if image.ndim == 2:
return np.array(_fast_nl_means_denoising_2d(image, patch_size,
patch_distance, h))
return np.array(_fast_nl_means_denoising_2d(image, s=patch_size,
d=patch_distance, h=h))
else:
raise ValueError("Fast non local means denoising is only possible for \
2D grayscale images.")