# 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.

```import numpy as np
import matplotlib.pyplot as plt

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

image = (data.coins()).astype(np.uint16) * 16
selem = disk(20)

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

fig, axes = plt.subplots(nrows=3, figsize=(8, 10))
ax0, ax1, ax2 = axes

ax0.imshow(np.hstack((image, percentile_result)))
ax0.set_title('Percentile mean')
ax0.axis('off')

ax1.imshow(np.hstack((image, bilateral_result)))
ax1.set_title('Bilateral mean')
ax1.axis('off')

ax2.imshow(np.hstack((image, normal_result)))
ax2.set_title('Local mean')
ax2.axis('off')

plt.show()
```