diff --git a/doc/examples/plot_edge_filter.py b/doc/examples/plot_edge_filter.py
index 0bb6c567..96b654a8 100644
--- a/doc/examples/plot_edge_filter.py
+++ b/doc/examples/plot_edge_filter.py
@@ -12,7 +12,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from skimage.data import camera
-from skimage.filters import roberts, sobel, scharr
+from skimage.filters import roberts, sobel, scharr, prewitt
 
 
 image = camera()
@@ -35,18 +35,23 @@ plt.tight_layout()
 .. image:: PLOT2RST.current_figure
 
 Different operators compute different finite-difference approximations of the
-gradient. For example, the Scharr filter results in a better rotational
-variance than other filters such as the Sobel filter [1]_ [2]_. The difference
-between the two filters is illustrated below on an image that is the
-discretization of a rotation-invariant continuous function. The discrepancy
-between the two filters is stronger for regions of the image where the
+gradient. For example, the Scharr filter results in a less rotational variance
+than the Sobel filter that is in turn better than the Prewitt filter [1]_ [2]_
+[3]_. The difference between the Prewitt and Sobel filters and the Scharr filter
+is illustrated below with an image that is the discretization of a rotation-
+invariant continuous function. The discrepancy between the Prewitt and Sobel
+filters, and the Scharr filter is stronger for regions of the image where the
 direction of the gradient is close to diagonal, and for regions with high
-spatial frequencies.
+spatial frequencies. For the example image the differences between the filter
+results are very small and the filter results are visually almost
+indistinguishable.
 
-.. [1] http://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
+.. [1] https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
 
 .. [2] B. Jaehne, H. Scharr, and S. Koerkel. Principles of filter design. In
        Handbook of Computer Vision and Applications. Academic Press, 1999.
+
+.. [3] https://en.wikipedia.org/wiki/Prewitt_operator
 """
 
 x, y = np.ogrid[:100, :100]
@@ -55,27 +60,31 @@ img = np.exp(1j * np.hypot(x, y)**1.3 / 20.).real
 
 edge_sobel = sobel(img)
 edge_scharr = scharr(img)
+edge_prewitt = prewitt(img)
+
+diff_scharr_prewitt = edge_scharr - edge_prewitt
+diff_scharr_sobel = edge_scharr - edge_sobel
+max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel))
 
 fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
 
-ax0.imshow(edge_sobel, cmap=plt.cm.gray)
-ax0.set_title('Sobel Edge Detection')
+ax0.imshow(img, cmap=plt.cm.gray)
+ax0.set_title('Original image')
 ax0.axis('off')
 
 ax1.imshow(edge_scharr, cmap=plt.cm.gray)
 ax1.set_title('Scharr Edge Detection')
 ax1.axis('off')
 
-ax2.imshow(img, cmap=plt.cm.gray)
-ax2.set_title('Original image')
+ax2.imshow(diff_scharr_prewitt, cmap=plt.cm.jet, vmax=max_diff)
+ax2.set_title('Scharr - Prewitt')
 ax2.axis('off')
 
-ax3.imshow(edge_scharr - edge_sobel, cmap=plt.cm.jet)
-ax3.set_title('difference (Scharr - Sobel)')
+ax3.imshow(diff_scharr_sobel, cmap=plt.cm.jet, vmax=max_diff)
+ax3.set_title('Scharr - Sobel')
 ax3.axis('off')
 
 plt.tight_layout()
-
 plt.show()
 
 """