mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)
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.
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 averaged.
Input image.
The neighborhood expressed as a 2-D array of 1's and 0's.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Output image.
Apply a flat kernel bilateral filter.
>>> from skimage import dataSee :
... from skimage.morphology import disk
... from skimage.filters.rank import mean_bilateral
... img = data.camera().astype(np.uint16)
... bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.filters.rank.bilateral.mean_bilateral
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