diff --git a/skimage/deconvolution/tests/camera_unsup.npy b/skimage/deconvolution/tests/camera_unsup.npy
index a10fbd76..96f4e666 100644
Binary files a/skimage/deconvolution/tests/camera_unsup.npy and b/skimage/deconvolution/tests/camera_unsup.npy differ
diff --git a/skimage/deconvolution/tests/camera_wiener.npy b/skimage/deconvolution/tests/camera_wiener.npy
index f095ff8c..7152aba1 100644
Binary files a/skimage/deconvolution/tests/camera_wiener.npy and b/skimage/deconvolution/tests/camera_wiener.npy differ
diff --git a/skimage/deconvolution/tests/test_deconvolution.py b/skimage/deconvolution/tests/test_deconvolution.py
index 542c914b..e7fe0d28 100644
--- a/skimage/deconvolution/tests/test_deconvolution.py
+++ b/skimage/deconvolution/tests/test_deconvolution.py
@@ -8,7 +8,7 @@ test_img = camera().astype(np.float)
 
 
 def test_wiener():
-    psf = np.ones((5, 5))
+    psf = np.ones((5, 5)) / 25
     data = convolve2d(test_img, psf, 'same')
     np.random.seed(0)
     data += 0.1 * data.std() * np.random.standard_normal(data.shape)
@@ -19,7 +19,7 @@ def test_wiener():
 
 
 def test_unsupervised_wiener():
-    psf = np.ones((5, 5))
+    psf = np.ones((5, 5)) / 25
     data = convolve2d(test_img, psf, 'same')
     np.random.seed(0)
     data += 0.1 * data.std() * np.random.standard_normal(data.shape)
diff --git a/skimage/deconvolution/wiener.py b/skimage/deconvolution/wiener.py
index fc4ea2f1..05b0c854 100644
--- a/skimage/deconvolution/wiener.py
+++ b/skimage/deconvolution/wiener.py
@@ -82,7 +82,7 @@ def wiener(data, psf, reg_val, reg=None, real=True):
     >>> from skimage import color, data, deconvolution
     >>> lena = color.rgb2gray(data.lena())
     >>> from scipy.signal import convolve2d
-    >>> psf = np.ones((5, 5))
+    >>> psf = np.ones((5, 5)) / 25
     >>> lena = convolve2d(lena, psf, 'same')
     >>> lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
     >>> deconvolved_lena = deconvolution.wiener(lena, psf, 1100)
@@ -212,7 +212,7 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
     >>> from skimage import color, data, deconvolution
     >>> lena = color.rgb2gray(data.lena())
     >>> from scipy.signal import convolve2d
-    >>> psf = np.ones((5, 5))
+    >>> psf = np.ones((5, 5)) / 25
     >>> lena = convolve2d(lena, psf, 'same')
     >>> lena += 0.1 * lena.std() * np.random.standard_normal(lena.shape)
     >>> deconvolved_lena = deconvolution.unsupervised_wiener(lena, psf)
@@ -258,6 +258,7 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
     areg2 = np.abs(reg)**2
     atf2 = np.abs(trans_fct)**2
 
+    data_size = data.size
     data = uft.urfft2(data.astype(np.float))
 
     # Gibbs sampling
@@ -279,12 +280,12 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
             params['callback'](x_sample)
 
         # sample of Eq. 31 p(gn | x^k, gx^k, y)
-        gn_chain.append(npr.gamma(data.size / 2,
+        gn_chain.append(npr.gamma(data_size / 2,
                                   2 / uft.image_quad_norm(data - x_sample *
                                                           trans_fct)))
 
         # sample of Eq. 31 p(gx | x^k, gn^k-1, y)
-        gx_chain.append(npr.gamma((data.size - 1) / 2,
+        gx_chain.append(npr.gamma((data_size - 1) / 2,
                                   2 / uft.image_quad_norm(x_sample * reg)))
 
         # current empirical average