diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py
index ce7bcb2d..db230d8b 100644
--- a/doc/examples/plot_entropy.py
+++ b/doc/examples/plot_entropy.py
@@ -10,10 +10,11 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.filter.rank import entropy
 from skimage.morphology import disk
+from skimage.util import img_as_ubyte
 
 
 # defining a 8- and a 16-bit test images
-a8 = data.camera()
+a8 = img_as_ubyte(data.camera())
 a16 = a8.astype(np.uint16) * 4
 
 ent8 = entropy(a8, disk(5)) # pixel value contain 10x the local entropy
diff --git a/doc/examples/plot_ihc_color_separation.py b/doc/examples/plot_ihc_color_separation.py
index b0b4a16c..f89f28fe 100644
--- a/doc/examples/plot_ihc_color_separation.py
+++ b/doc/examples/plot_ihc_color_separation.py
@@ -22,6 +22,7 @@ import matplotlib.pyplot as plt
 from skimage import data
 from skimage.color import rgb2hed
 
+
 ihc_rgb = data.immunohistochemistry()
 ihc_hed = rgb2hed(ihc_rgb)
 
diff --git a/doc/examples/plot_local_equalize.py b/doc/examples/plot_local_equalize.py
index efa04c33..1fd8325f 100644
--- a/doc/examples/plot_local_equalize.py
+++ b/doc/examples/plot_local_equalize.py
@@ -19,15 +19,14 @@ References
 .. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
 
 """
+import numpy as np
+import matplotlib.pyplot as plt
 
 from skimage import data
 from skimage.util.dtype import dtype_range
+from skimage.util import img_as_ubyte
 from skimage import exposure
 from skimage.morphology import disk
-
-import matplotlib.pyplot as plt
-
-import numpy as np
 from skimage.filter import rank
 
 
@@ -58,7 +57,7 @@ def plot_img_and_hist(img, axes, bins=256):
 
 
 # Load an example image
-img = data.moon()
+img = img_as_ubyte(data.moon())
 
 # Contrast stretching
 p2 = np.percentile(img, 2)
diff --git a/doc/examples/plot_local_otsu.py b/doc/examples/plot_local_otsu.py
index aee0ef6a..3a321780 100644
--- a/doc/examples/plot_local_otsu.py
+++ b/doc/examples/plot_local_otsu.py
@@ -18,12 +18,12 @@ The example compares the local threshold with the global threshold.
 import matplotlib.pyplot as plt
 
 from skimage import data
-from skimage.morphology.selem import disk
-import skimage.filter.rank as rank
-from skimage.filter import threshold_otsu
+from skimage.morphology import disk
+from skimage.filter import threshold_otsu, rank
+from skimage.util import img_as_ubyte
 
 
-p8 = data.page()
+p8 = img_as_ubyte(data.page())
 
 radius = 10
 selem = disk(radius)
diff --git a/doc/examples/plot_marked_watershed.py b/doc/examples/plot_marked_watershed.py
index 31e7a245..d7f2c354 100644
--- a/doc/examples/plot_marked_watershed.py
+++ b/doc/examples/plot_marked_watershed.py
@@ -16,13 +16,14 @@ See Wikipedia_ for more details on the algorithm.
 
 from scipy import ndimage
 import matplotlib.pyplot as plt
+
 from skimage.morphology import watershed, disk
 from skimage import data
-
-# original data
 from skimage.filter import rank
+from skimage.util import img_as_ubyte
 
-image = data.camera()
+
+image = img_as_ubyte(data.camera())
 
 # denoise image
 denoised = rank.median(image, disk(2))