`skimage.filters.rank.bilateral:mean_bilateral`

source: /dev/scikit-image/src/skimage/filters/rank/bilateral.py :60

Signature

   def     mean_bilateral (    image    ,    footprint    ,    out    =  None   ,    mask    =  None   ,    shift_x    =  0   ,    shift_y    =  0   ,    s0    =  10   ,    s1    =  10     )

Summary

Apply a flat kernel bilateral filter.

Extended Summary

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 averaged.

Parameters

image : 2-D array (uint8, uint16): Input image.
footprint : 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 `image`: 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 footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).
s0, s1 : int: Define the [s0, s1] interval around the grayvalue of the center pixel to be considered for computing the value.

Returns

out : 2-D array, same dtype as input `image`: Output image.

Examples

import numpy as np
from skimage import data
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)

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Aliases

skimage.filters.rank.mean_bilateral