diff --git a/skimage/deconvolution/uft.py b/skimage/deconvolution/uft.py
index 6f80a22d..a592f8d7 100644
--- a/skimage/deconvolution/uft.py
+++ b/skimage/deconvolution/uft.py
@@ -332,17 +332,6 @@ def image_quad_norm(inarray):
         return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)
 
 
-def crandn(shape):
-    """white complex gaussian noise
-
-    Generate directly the unitary Fourier transform of white gaussian
-    noise noise field (with given shape) of zero mean and variance
-    unity (ie N(0,1)).
-    """
-    return np.sqrt(0.5) * (np.random.standard_normal(shape) +
-                           1j * np.random.standard_normal(shape))
-
-
 def ir2tf(imp_resp, shape, dim=None, real=True):
     """Compute the transfer function of IR
 
diff --git a/skimage/deconvolution/wiener.py b/skimage/deconvolution/wiener.py
index 2247d619..762499c5 100644
--- a/skimage/deconvolution/wiener.py
+++ b/skimage/deconvolution/wiener.py
@@ -266,7 +266,9 @@ def unsupervised_wiener(data, psf, reg=None, user_params=None):
 
         # weighing (correlation in direct space)
         precision = gn_chain[-1] * atf2 + gx_chain[-1] * areg2  # Eq. 29
-        excursion = uft.crandn(data.shape) / np.sqrt(precision)
+        excursion = np.sqrt(0.5) / np.sqrt(precision) * (
+            np.random.standard_normal(data.shape) +
+            1j * np.random.standard_normal(data.shape))
 
         # mean Eq. 30 (RLS for fixed gn, gamma0 and gamma1 ...)
         wiener_filter = gn_chain[-1] * np.conj(trans_fct) / precision