diff --git a/skimage/filter/_denoise.py b/skimage/filter/_denoise.py
index 043399e0..c0aed443 100644
--- a/skimage/filter/_denoise.py
+++ b/skimage/filter/_denoise.py
@@ -30,14 +30,6 @@ def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
     -----
     Rudin, Osher and Fatemi algorithm.
 
-    Examples
-    --------
-    >>> x, y, z = np.ogrid[0:40, 0:40, 0:40]
-    >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
-    >>> mask = mask.astype(np.float)
-    >>> mask += 0.2 * np.random.randn(*mask.shape)
-    >>> res = denoise_tv_chambolle(mask, weight=100)
-
     """
 
     px = np.zeros_like(im)
@@ -121,13 +113,6 @@ def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
            applications, Journal of Mathematical Imaging and Vision,
            Springer, 2004, 20, 89-97.
 
-    Examples
-    --------
-    >>> from skimage import color, data
-    >>> lena = color.rgb2gray(data.lena())
-    >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
-    >>> denoised_lena = denoise_tv_chambolle(lena, weight=60)
-
     """
 
     px = np.zeros_like(im)
@@ -224,13 +209,13 @@ def denoise_tv_chambolle(im, weight=50, eps=2.e-4, n_iter_max=200,
     2D example on Lena image:
 
     >>> from skimage import color, data
-    >>> lena = color.rgb2gray(data.lena())
+    >>> lena = color.rgb2gray(data.lena())[:50, :50]
     >>> lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
     >>> denoised_lena = denoise_tv_chambolle(lena, weight=60)
 
     3D example on synthetic data:
 
-    >>> x, y, z = np.ogrid[0:40, 0:40, 0:40]
+    >>> x, y, z = np.ogrid[0:20, 0:20, 0:20]
     >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
     >>> mask = mask.astype(np.float)
     >>> mask += 0.2*np.random.randn(*mask.shape)
diff --git a/skimage/filter/thresholding.py b/skimage/filter/thresholding.py
index 7f980387..2ce8a3e1 100644
--- a/skimage/filter/thresholding.py
+++ b/skimage/filter/thresholding.py
@@ -57,7 +57,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
     Examples
     --------
     >>> from skimage.data import camera
-    >>> image = camera()
+    >>> image = camera()[:50, :50]
     >>> binary_image1 = threshold_adaptive(image, 15, 'mean')
     >>> func = lambda arr: arr.mean()
     >>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)