diff --git a/skimage/filters/__init__.py b/skimage/filters/__init__.py
index 9dd0b4df..708f5d53 100644
--- a/skimage/filters/__init__.py
+++ b/skimage/filters/__init__.py
@@ -5,7 +5,7 @@ from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
                     prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
                     roberts, roberts_positive_diagonal,
                     roberts_negative_diagonal, roberts_pos_diag,
-                    roberts_neg_diag)
+                    roberts_neg_diag, laplace)
 from ._rank_order import rank_order
 from ._gabor import gabor_kernel, gabor
 from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
@@ -62,6 +62,7 @@ __all__ = ['inverse',
            'roberts_negative_diagonal',
            'roberts_pos_diag',
            'roberts_neg_diag',
+           'laplace',
            'denoise_tv_chambolle',
            'denoise_bilateral',
            'denoise_tv_bregman',
diff --git a/skimage/filters/edges.py b/skimage/filters/edges.py
index 1eeed4de..4cf083a3 100644
--- a/skimage/filters/edges.py
+++ b/skimage/filters/edges.py
@@ -14,6 +14,7 @@ from .. import img_as_float
 from .._shared.utils import assert_nD, deprecated
 from scipy.ndimage import convolve, binary_erosion, generate_binary_structure
 
+from ..restoration.uft import laplacian
 
 EROSION_SELEM = generate_binary_structure(2, 2)
 
@@ -175,6 +176,7 @@ def hsobel(image, mask=None):
 
     Parameters
     ----------
+
     image : 2-D array
         Image to process.
     mask : 2-D array, optional
@@ -762,3 +764,35 @@ def roberts_negative_diagonal(image, mask=None):
 
     """
     return np.abs(roberts_neg_diag(image, mask))
+
+def laplace(image, ksize=3, mask=None):
+    """Find the edges of an image using the Laplace operator.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image to process.
+    ksize : int, optional
+        Define the size of the discrete Laplacian operator such that it
+        will have a size of (ksize,) * image.ndim.
+    mask : ndarray, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : ndarray
+        The Laplace edge map.
+
+    Notes
+    -----
+    The Laplacian operator is generated using the function 
+    skimage.restoration.uft.laplacian().
+
+    """
+    image = img_as_float(image)
+    # Create the discrete Laplacian operator - We keep only the real part of the filter
+    _, laplace_op = laplacian(image.ndim, (ksize, ) * image.ndim)
+    result = convolve(image, laplace_op)
+    return _mask_filter_result(result, mask)
diff --git a/skimage/filters/tests/test_edges.py b/skimage/filters/tests/test_edges.py
index d2c0f469..0fbaa4ac 100644
--- a/skimage/filters/tests/test_edges.py
+++ b/skimage/filters/tests/test_edges.py
@@ -335,6 +335,34 @@ def test_vprewitt_horizontal():
     assert_allclose(result, 0)
 
 
+def test_laplace_zeros():
+    """Laplace on a square image."""
+    # Create a synthetic 2D image
+    image = np.zeros((9, 9))
+    image[3:-3, 3:-3] = 1
+    result = filters.laplace(image)
+    res_chk = np.array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0.,  0., -1., -1., -1.,  0.,  0.,  0.],
+                        [ 0.,  0., -1.,  2.,  1.,  2., -1.,  0.,  0.],
+                        [ 0.,  0., -1.,  1.,  0.,  1., -1.,  0.,  0.],
+                        [ 0.,  0., -1.,  2.,  1.,  2., -1.,  0.,  0.],
+                        [ 0.,  0.,  0., -1., -1., -1.,  0.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
+    assert_allclose(result, res_chk)
+
+
+def test_laplace_mask():
+    """Laplace on a masked array should be zero."""
+    # Create a synthetic 2D image
+    image = np.zeros((9, 9))
+    image[3:-3, 3:-3] = 1
+    # Define the mask
+    result = filters.laplace(image, ksize=3, mask=np.zeros((9, 9), bool))
+    assert (np.all(result == 0))
+
+
 def test_horizontal_mask_line():
     """Horizontal edge filters mask pixels surrounding input mask."""
     vgrad, _ = np.mgrid[:1:11j, :1:11j]  # vertical gradient with spacing 0.1
@@ -351,7 +379,6 @@ def test_horizontal_mask_line():
         result = grad_func(vgrad, mask)
         yield assert_close, result, expected
 
-
 def test_vertical_mask_line():
     """Vertical edge filters mask pixels surrounding input mask."""
     _, hgrad = np.mgrid[:1:11j, :1:11j]  # horizontal gradient with spacing 0.1