diff --git a/doc/examples/plot_lena_bilateral_denoise.py b/doc/examples/plot_lena_bilateral_denoise.py
index fbee7d67..9fd20285 100644
--- a/doc/examples/plot_lena_bilateral_denoise.py
+++ b/doc/examples/plot_lena_bilateral_denoise.py
@@ -1,26 +1,22 @@
 """
 ====================================================
-Denoising the picture of Lena using total variation
+Denoising the picture of Lena using bilateral filter
 ====================================================
 
 In this example, we denoise a noisy version of the picture of Lena
-using the total variation denoising filter. The result of this filter
-is an image that has a minimal total variation norm, while being as
-close to the initial image as possible. The total variation is the L1
-norm of the gradient of the image, and minimizing the total variation
-typically produces "posterized" images with flat domains separated by
-sharp edges.
-
-It is possible to change the degree of posterization by controlling
-the tradeoff between denoising and faithfulness to the original image.
+using an approximation of a bilateral filter.
+The pixels used to compute a local mean respect these conditions:
+- be close to the central pixel, i.e. belong to the given structuring element.
+- have a similar gray level, similarity is fixed by an interval [-s0,+s1] centered on the central pixel gray level.
 
+The filter used is an approximation of a classical bilateral filter in the sens that kernel are usually gaussian
+both in spatial and spectral dimensions.
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage import data, color, img_as_ubyte
-from skimage.filter import tv_denoise
 from skimage.rank import bilateral_mean
 from skimage.morphology import disk
 
@@ -44,12 +40,11 @@ plt.axis('off')
 plt.title('bilateral denoising', fontsize=20)
 
 selem = disk(30)
-bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=30,s1=30)
+bilateral_denoised = bilateral_mean(noisy.astype(np.uint8), selem=selem,s0=40,s1=40)
 plt.subplot(133)
 plt.imshow(bilateral_denoised, cmap=plt.cm.gray, vmin=40, vmax=220)
 plt.axis('off')
 plt.title('(more) bilateral denoising', fontsize=20)
 
-plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,
-                    right=1)
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0, left=0,right=1)
 plt.show()
diff --git a/doc/examples/plot_local_threshold.py b/doc/examples/plot_local_threshold.py
index 3b9a42fb..01077571 100644
--- a/doc/examples/plot_local_threshold.py
+++ b/doc/examples/plot_local_threshold.py
@@ -24,7 +24,7 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.filter import threshold_otsu, threshold_adaptive
 
-from skimage.rank import threshold
+from skimage.rank import threshold,morph_contr_enh
 from skimage.morphology import disk
 
 
@@ -38,9 +38,10 @@ binary_adaptive = threshold_adaptive(image, block_size, offset=10)
 
 selem = disk(10)
 loc_thresh = threshold(image,selem=selem)
+loc_morph_contr_enh = morph_contr_enh(image,selem=selem)
 
-fig, axes = plt.subplots(nrows=4, figsize=(7, 8))
-ax0, ax1, ax2, ax3 = axes
+fig, axes = plt.subplots(nrows=5, figsize=(7, 8))
+ax0, ax1, ax2, ax3, ax4 = axes
 plt.gray()
 
 ax0.imshow(image)
@@ -55,6 +56,9 @@ ax2.set_title('Adaptive thresholding')
 ax3.imshow(loc_thresh)
 ax3.set_title('Local thresholding')
 
+ax4.imshow(loc_morph_contr_enh)
+ax4.set_title('Local morphological contrast enhancement')
+
 
 for ax in axes:
     ax.axis('off')
diff --git a/skimage/rank/rank.py b/skimage/rank/rank.py
index 946f5a49..42e5c23e 100644
--- a/skimage/rank/rank.py
+++ b/skimage/rank/rank.py
@@ -1,6 +1,6 @@
 """rank.py - rankfilter for local (custom kernel) maximum, minimum, median, mean, auto-level, egalize, etc
 
-The local histogram is computed using a sliding window similar to the method described in 
+The local histogram is computed using a sliding window similar to the method described in
 
 Reference: Huang, T. ,Yang, G. ;  Tang, G.. "A fast two-dimensional median filtering algorithm",
 IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.