diff --git a/doc/examples/plot_edge_filter.py b/doc/examples/plot_edge_filter.py
index 0bb6c567..66907f4f 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()
@@ -36,17 +36,21 @@ plt.tight_layout()
 
 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
-direction of the gradient is close to diagonal, and for regions with high
-spatial frequencies.
+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 on 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. The Sobel filter is less rotationally invariant than the
+Sobel filter.
 
-.. [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]
@@ -55,25 +59,38 @@ img = np.exp(1j * np.hypot(x, y)**1.3 / 20.).real
 
 edge_sobel = sobel(img)
 edge_scharr = scharr(img)
+edge_prewitt = prewitt(img)
 
-fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)
+fig, ((ax0, ax1, ax2), (ax3, ax4, ax5)) = plt.subplots(nrows=2, ncols=3)
 
-ax0.imshow(edge_sobel, cmap=plt.cm.gray)
-ax0.set_title('Sobel Edge Detection')
+ax0.imshow(edge_prewitt, cmap=plt.cm.gray)
+ax0.set_title('Prewitt Edge Detection')
 ax0.axis('off')
 
-ax1.imshow(edge_scharr, cmap=plt.cm.gray)
-ax1.set_title('Scharr Edge Detection')
+ax1.imshow(edge_sobel, cmap=plt.cm.gray)
+ax1.set_title('Sobel Edge Detection')
 ax1.axis('off')
 
-ax2.imshow(img, cmap=plt.cm.gray)
-ax2.set_title('Original image')
+ax2.imshow(edge_scharr, cmap=plt.cm.gray)
+ax2.set_title('Scharr Edge Detection')
 ax2.axis('off')
 
-ax3.imshow(edge_scharr - edge_sobel, cmap=plt.cm.jet)
-ax3.set_title('difference (Scharr - Sobel)')
+ax3.imshow(img, cmap=plt.cm.gray)
+ax3.set_title('Original image')
 ax3.axis('off')
 
+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))
+
+ax4.imshow(diff_scharr_prewitt, cmap=plt.cm.jet, vmax=max_diff)
+ax4.set_title('difference (Scharr - Prewitt)')
+ax4.axis('off')
+
+ax5.imshow(diff_scharr_sobel, cmap=plt.cm.jet, vmax=max_diff)
+ax5.set_title('difference (Scharr - Sobel)')
+ax5.axis('off')
+
 plt.tight_layout()
 
 plt.show()