sum_bilateral
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skimage.filters.rank.sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)
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Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem).
Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.
Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed.
Note that the sum may overflow depending on the data type of the input array.
Parameters: image : 2-D array (uint8, uint16)
Input image.
selem : 2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
out : 2-D array (same dtype as input)
If None, a new array is allocated.
mask : ndarray
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
shift_x, shift_y : int
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
s0, s1 : int
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Returns: out : 2-D array (same dtype as input image)
Output image.
See also
skimage.filters.denoise_bilateral
Examples
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
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