Mean filtersΒΆ

This example compares the following mean filters of the rank filter package:

  • local mean: all pixels belonging to the structuring element to compute average gray level.

  • percentile mean: only use values between percentiles p0 and p1 (here 10% and 90%).

  • bilateral mean: only use pixels of the structuring element having a gray level situated inside g-s0 and g+s1 (here g-500 and g+500)

Percentile and usual mean give here similar results, these filters smooth the complete image (background and details). Bilateral mean exhibits a high filtering rate for continuous area (i.e. background) while higher image frequencies remain untouched.

Original, Percentile mean, Bilateral mean, Local mean
import matplotlib.pyplot as plt

from skimage import data
from skimage.morphology import disk
from skimage.filters import rank

image = data.coins()
footprint = disk(20)

percentile_result = rank.mean_percentile(
    image, footprint=footprint, p0=.1, p1=.9
bilateral_result = rank.mean_bilateral(
    image, footprint=footprint, s0=500, s1=500
normal_result = rank.mean(image, footprint=footprint)

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10),
                         sharex=True, sharey=True)
ax = axes.ravel()

titles = ['Original', 'Percentile mean', 'Bilateral mean', 'Local mean']
imgs = [image, percentile_result, bilateral_result, normal_result]
for n in range(0, len(imgs)):


Total running time of the script: ( 0 minutes 0.393 seconds)

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