skimage2.filters.rank.sum_bilateral#
- skimage2.filters.rank.sum_bilateral(image, footprint, out=None, mask=None, shift_x=0, shift_y=0, s0=10, s1=10)[source]#
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 footprint (structuring element).
Radiometric similarity is defined by the graylevel interval [g-s0, g+s1] where g is the current pixel graylevel.
Only pixels belonging to the footprint AND having a graylevel inside this interval are summed.
Note that the sum may overflow depending on the data type of the input array.
- Parameters:
- image2-D array (uint8, uint16)
Input image.
- footprint2-D array
The neighborhood expressed as a 2-D array of 1’s and 0’s.
- out2-D array, same dtype as input
image If None, a new array is allocated.
- maskndarray
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_yint
Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).
- s0, s1int
Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value.
- Returns:
- out2-D array, same dtype as input
image Output image.
- out2-D array, same dtype as input
Examples
>>> import numpy as np >>> from _skimage2 import data >>> from _skimage2.morphology import disk >>> from _skimage2.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
