diff --git a/doc/examples/plot_restoration.py b/doc/examples/plot_restoration.py
index 793377e9..fdffd953 100644
--- a/doc/examples/plot_restoration.py
+++ b/doc/examples/plot_restoration.py
@@ -46,11 +46,11 @@ fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
 
 plt.gray()
 
-ax[0].imshow(lena)
+ax[0].imshow(lena, vmin=deconvolved.min(), vmax=deconvolved.max())
 ax[0].axis('off')
 ax[0].set_title('Data')
 
-ax[1].imshow(deconvolved, vmax=lena.max())
+ax[1].imshow(deconvolved)
 ax[1].axis('off')
 ax[1].set_title('Self tuned restoration')
 
diff --git a/skimage/restoration/deconvolution.py b/skimage/restoration/deconvolution.py
index 5d3bd55a..367af563 100644
--- a/skimage/restoration/deconvolution.py
+++ b/skimage/restoration/deconvolution.py
@@ -153,8 +153,8 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None):
 
     Return the deconvolution with a Wiener-Hunt approach, where the
     hyperparameters are automatically estimated. The algorithm is a
-    stochastic iterative process (Gibbs sampler) described in [1]. See
-    also `wiener` function.
+    stochastic iterative process (Gibbs sampler) described in ref
+    belo]. See also `wiener` function.
 
     Parameters
     ----------