Rename mean filter example and improve description

doc/examples/plot_rank_mean.py

@@ -0,0 +1,45 @@
+"""
+============
+Mean filters
+============
+
+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
+import skimage.filter.rank as rank
+
+a16 = (data.coins()).astype('uint16') * 16
+selem = disk(20)
+
+f1 = rank.percentile_mean(a16, selem=selem, p0=.1, p1=.9)
+f2 = rank.bilateral_mean(a16, selem=selem, s0=500, s1=500)
+f3 = rank.mean(a16, selem=selem)
+
+# display results
+fig, axes = plt.subplots(nrows=3, figsize=(15, 10))
+ax0, ax1, ax2 = axes
+
+ax0.imshow(np.hstack((a16, f1)))
+ax0.set_title('percentile mean')
+ax1.imshow(np.hstack((a16, f2)))
+ax1.set_title('bilateral mean')
+ax2.imshow(np.hstack((a16, f3)))
+ax2.set_title('local mean')
+plt.show()