diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py
deleted file mode 100644
index 206b4027..00000000
--- a/skimage/filter/tests/test_denoise.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import numpy as np
-from numpy.testing import run_module_suite, assert_raises, assert_equal
-
-from skimage import filter, data, color, img_as_float
-
-
-np.random.seed(1234)
-
-
-lena = img_as_float(data.lena()[:256, :256])
-lena_gray = color.rgb2gray(lena)
-
-
-def test_denoise_tv_chambolle_2d():
-    # lena image
-    img = lena_gray
-    # add noise to lena
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    # clip noise so that it does not exceed allowed range for float images.
-    img = np.clip(img, 0, 1)
-    # denoise
-    denoised_lena = filter.denoise_tv_chambolle(img, weight=60.0)
-    # which dtype?
-    assert denoised_lena.dtype in [np.float, np.float32, np.float64]
-    from scipy import ndimage
-    grad = ndimage.morphological_gradient(img, size=((3, 3)))
-    grad_denoised = ndimage.morphological_gradient(
-        denoised_lena, size=((3, 3)))
-    # test if the total variation has decreased
-    assert grad_denoised.dtype == np.float
-    assert (np.sqrt((grad_denoised**2).sum())
-            < np.sqrt((grad**2).sum()) / 2)
-
-
-def test_denoise_tv_chambolle_multichannel():
-    denoised0 = filter.denoise_tv_chambolle(lena[..., 0], weight=60.0)
-    denoised = filter.denoise_tv_chambolle(lena, weight=60.0, multichannel=True)
-    assert_equal(denoised[..., 0], denoised0)
-
-
-def test_denoise_tv_chambolle_float_result_range():
-    # lena image
-    img = lena_gray
-    int_lena = np.multiply(img, 255).astype(np.uint8)
-    assert np.max(int_lena) > 1
-    denoised_int_lena = filter.denoise_tv_chambolle(int_lena, weight=60.0)
-    # test if the value range of output float data is within [0.0:1.0]
-    assert denoised_int_lena.dtype == np.float
-    assert np.max(denoised_int_lena) <= 1.0
-    assert np.min(denoised_int_lena) >= 0.0
-
-
-def test_denoise_tv_chambolle_3d():
-    """Apply the TV denoising algorithm on a 3D image representing a sphere."""
-    x, y, z = np.ogrid[0:40, 0:40, 0:40]
-    mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
-    mask = 100 * mask.astype(np.float)
-    mask += 60
-    mask += 20 * np.random.random(mask.shape)
-    mask[mask < 0] = 0
-    mask[mask > 255] = 255
-    res = filter.denoise_tv_chambolle(mask.astype(np.uint8), weight=100)
-    assert res.dtype == np.float
-    assert res.std() * 255 < mask.std()
-
-    # test wrong number of dimensions
-    assert_raises(ValueError, filter.denoise_tv_chambolle,
-                  np.random.random((8, 8, 8, 8)))
-
-
-def test_denoise_tv_bregman_2d():
-    img = lena_gray
-    # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    img = np.clip(img, 0, 1)
-
-    out1 = filter.denoise_tv_bregman(img, weight=10)
-    out2 = filter.denoise_tv_bregman(img, weight=5)
-
-    # make sure noise is reduced
-    assert img.std() > out1.std()
-    assert out1.std() > out2.std()
-
-
-def test_denoise_tv_bregman_float_result_range():
-    # lena image
-    img = lena_gray
-    int_lena = np.multiply(img, 255).astype(np.uint8)
-    assert np.max(int_lena) > 1
-    denoised_int_lena = filter.denoise_tv_bregman(int_lena, weight=60.0)
-    # test if the value range of output float data is within [0.0:1.0]
-    assert denoised_int_lena.dtype == np.float
-    assert np.max(denoised_int_lena) <= 1.0
-    assert np.min(denoised_int_lena) >= 0.0
-
-
-def test_denoise_tv_bregman_3d():
-    img = lena
-    # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    img = np.clip(img, 0, 1)
-
-    out1 = filter.denoise_tv_bregman(img, weight=10)
-    out2 = filter.denoise_tv_bregman(img, weight=5)
-
-    # make sure noise is reduced
-    assert img.std() > out1.std()
-    assert out1.std() > out2.std()
-
-
-def test_denoise_bilateral_2d():
-    img = lena_gray
-    # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    img = np.clip(img, 0, 1)
-
-    out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
-    out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
-
-    # make sure noise is reduced
-    assert img.std() > out1.std()
-    assert out1.std() > out2.std()
-
-
-def test_denoise_bilateral_3d():
-    img = lena
-    # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    img = np.clip(img, 0, 1)
-
-    out1 = filter.denoise_bilateral(img, sigma_range=0.1, sigma_spatial=20)
-    out2 = filter.denoise_bilateral(img, sigma_range=0.2, sigma_spatial=30)
-
-    # make sure noise is reduced
-    assert img.std() > out1.std()
-    assert out1.std() > out2.std()
-
-
-def test_nl_means_denoising_2d():
-    img = np.zeros((40, 40))
-    img[10:-10, 10:-10] = 1.
-    img += 0.3*np.random.randn(*img.shape)
-    denoised = filter.nl_means_denoising(img, 7, 5, 0.1)
-    # make sure noise is reduced
-    assert img.std() > denoised.std()
-
-
-def test_nl_means_denoising_2drgb():
-    # reduce image size because nl means is very slow
-    img = lena[-100:, -100:]
-    # add some random noise
-    img += 0.5 * img.std() * np.random.random(img.shape)
-    img = np.clip(img, 0, 1)
-    denoised = filter.nl_means_denoising(img, 7, 9, 0.08)
-    # make sure noise is reduced
-    assert img.std() > denoised.std()
-
-
-def test_nl_means_denoising_3d():
-    img = np.zeros((20, 20, 10))
-    img[5:-5, 5:-5, 3:-3] = 1.
-    img += 0.3*np.random.randn(*img.shape)
-    denoised = filter.nl_means_denoising(img, 5, 4, 0.1)
-    # make sure noise is reduced
-    assert img.std() > denoised.std()
-
-
-if __name__ == "__main__":
-    run_module_suite()