diff --git a/doc/examples/plot_deconvolution.py b/doc/examples/plot_deconvolution.py
index fd94e77f..761193b4 100644
--- a/doc/examples/plot_deconvolution.py
+++ b/doc/examples/plot_deconvolution.py
@@ -48,9 +48,9 @@ ax[0].imshow(lena)
 ax[0].axis('off')
 ax[0].set_title('Data')
 
-ax[1].imshow(deconvolued)
+ax[1].imshow(deconvolued, vmax=lena.max())
 ax[1].axis('off')
-ax[1].set_title('Deconvolution')
+ax[1].set_title('Self tuned deconvolution')
 
 fig.subplots_adjust(wspace=0.02, hspace=0.2,
                     top=0.9, bottom=0.05, left=0, right=1)
diff --git a/skimage/deconvolution/tests/test_deconvolution.py b/skimage/deconvolution/tests/test_deconvolution.py
index e7fe0d28..be007300 100644
--- a/skimage/deconvolution/tests/test_deconvolution.py
+++ b/skimage/deconvolution/tests/test_deconvolution.py
@@ -30,4 +30,11 @@ def test_unsupervised_wiener():
 
 
 def test_richardson_lucy():
-    return True
+    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)
+    deconvolved, _ = deconvolution.richardson_lucy(data, psf, 5)
+
+    path = pjoin(dirname(abspath(__file__)), 'camera_rl.npy')
+    np.testing.assert_allclose(deconvolved, np.load(path))
diff --git a/skimage/deconvolution/wiener.py b/skimage/deconvolution/wiener.py
index b789d026..dac48bf8 100644
--- a/skimage/deconvolution/wiener.py
+++ b/skimage/deconvolution/wiener.py
@@ -28,7 +28,7 @@ from __future__ import division
 
 import numpy as np
 import numpy.random as npr
-from scipy.signal import convolve2d as conv2
+from scipy.signal import convolve2d
 
 import uft
 
@@ -330,13 +330,24 @@ def richardson_lucy(data, psf, iterations=50):
        The point spread function
 
     iterations : int
-       Number of iterations
+       Number of iterations. This parameter play to role of regularisation.
 
     Returns
     -------
     im_deconv : ndarray
        The deconvolved image
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skimage import color, data, deconvolution
+    >>> camera = color.rgb2gray(data.camera())
+    >>> from scipy.signal import convolve2d
+    >>> psf = np.ones((5, 5)) / 25
+    >>> camera = convolve2d(camera, psf, 'same')
+    >>> camera += 0.1 * camera.std() * np.random.standard_normal(camera.shape)
+    >>> deconvolved = deconvolution.richardson_lucy(camera, psf, 5)
+
     References
     ----------
     .. [2] http://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution
@@ -347,7 +358,7 @@ def richardson_lucy(data, psf, iterations=50):
     im_deconv = 0.5 * np.ones(data.shape)
     psf_mirror = psf[::-1, ::-1]
     for _ in range(iterations):
-        relative_blur = data / conv2(im_deconv, psf, 'same')
-        im_deconv *= conv2(relative_blur, psf_mirror, 'same')
+        relative_blur = data / convolve2d(im_deconv, psf, 'same')
+        im_deconv *= convolve2d(relative_blur, psf_mirror, 'same')
 
     return im_deconv