diff --git a/skimage/filter/__init__.py b/skimage/filter/__init__.py
index 4f8b129a..39d50376 100644
--- a/skimage/filter/__init__.py
+++ b/skimage/filter/__init__.py
@@ -3,6 +3,6 @@ from .ctmf import median_filter
 from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
                     hprewitt, vprewitt)
-from ._denoise import denoise_bilateral, denoise_tv, tv_denoise
+from ._denoise import denoise_bilateral, denoise_tv_bregman
 from ._rank_order import rank_order
 from .thresholding import threshold_otsu, threshold_adaptive
diff --git a/skimage/filter/_denoise.pyx b/skimage/filter/_denoise.pyx
index 436097f6..ea046a0a 100644
--- a/skimage/filter/_denoise.pyx
+++ b/skimage/filter/_denoise.pyx
@@ -179,7 +179,7 @@ def denoise_bilateral(image, int win_size=5, sigma_range=None,
     return np.squeeze(out)
 
 
-def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3):
+def denoise_tv_bregman(image, double weight, int max_iter=100, double eps=1e-3):
     """Perform total-variation denoising using split-Bregman optimization.
 
     Total-variation denoising (also know as total-variation regularization)
@@ -314,5 +314,3 @@ def denoise_tv(image, double weight, int max_iter=100, double eps=1e-3):
         i += 1
 
     return np.squeeze(u[1:-1, 1:-1])
-
-tv_denoise = deprecated('skimage.filter.denoise_tv')(denoise_tv)
diff --git a/skimage/filter/tests/test_denoise.py b/skimage/filter/tests/test_denoise.py
index 03c8c58a..6f77fdc7 100644
--- a/skimage/filter/tests/test_denoise.py
+++ b/skimage/filter/tests/test_denoise.py
@@ -14,8 +14,8 @@ def test_denoise_tv_2d():
     img += 0.5 * img.std() * np.random.random(img.shape)
     img = np.clip(img, 0, 1)
 
-    out1 = filter.denoise_tv(img, weight=10)
-    out2 = filter.denoise_tv(img, weight=5)
+    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()
@@ -27,7 +27,7 @@ def test_denoise_tv_float_result_range():
     img = lena_gray
     int_lena = np.multiply(img, 255).astype(np.uint8)
     assert np.max(int_lena) > 1
-    denoised_int_lena = filter.denoise_tv(int_lena, weight=60.0)
+    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
@@ -40,8 +40,8 @@ def test_denoise_tv_3d():
     img += 0.5 * img.std() * np.random.random(img.shape)
     img = np.clip(img, 0, 1)
 
-    out1 = filter.denoise_tv(img, weight=10)
-    out2 = filter.denoise_tv(img, weight=5)
+    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()