diff --git a/skimage/filter/rank/_crank8.pyx b/skimage/filter/rank/_crank8.pyx
index da716bd9..a0c3cc35 100644
--- a/skimage/filter/rank/_crank8.pyx
+++ b/skimage/filter/rank/_crank8.pyx
@@ -223,6 +223,26 @@ cdef inline np.uint8_t kernel_tophat(
 
     return < np.uint8_t > (i - g)
 
+cdef inline np.uint8_t kernel_noise_filter(
+        Py_ssize_t * histo, float pop, np.uint8_t g, float p0, float p1, Py_ssize_t s0,
+        Py_ssize_t s1):
+
+    cdef Py_ssize_t i
+    cdef Py_ssize_t min_i
+
+    for i in range(255, g, -1):
+        if histo[i]:
+            break
+    min_i = i-g
+    for i in range(0, g):
+        if histo[i]:
+            break
+    if g-i < min_i:
+        return < np.uint8_t > (g-i)
+    else:
+        return < np.uint8_t > min_i
+
+
 # -----------------------------------------------------------------
 # python wrappers
 # -----------------------------------------------------------------
@@ -380,3 +400,12 @@ def tophat(np.ndarray[np.uint8_t, ndim=2] image,
     """top hat
     """
     return _core8(kernel_tophat, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
+
+def noise_filter(np.ndarray[np.uint8_t, ndim=2] image,
+           np.ndarray[np.uint8_t, ndim=2] selem,
+           np.ndarray[np.uint8_t, ndim=2] mask=None,
+           np.ndarray[np.uint8_t, ndim=2] out=None,
+           char shift_x=0, char shift_y=0):
+    """top hat
+    """
+    return _core8(kernel_noise_filter, image, selem, mask, out, shift_x, shift_y, .0, .0, < Py_ssize_t > 0, < Py_ssize_t > 0)
diff --git a/skimage/filter/rank/demo/demo_single.py b/skimage/filter/rank/demo/demo_single.py
index 652f2cc8..3b7eaa95 100644
--- a/skimage/filter/rank/demo/demo_single.py
+++ b/skimage/filter/rank/demo/demo_single.py
@@ -19,6 +19,27 @@ if __name__ == '__main__':
     den = denoise_bilateral(a8,win_size=10,sigma_range=10,sigma_spatial=2)[:,:,0]
     f16b= rank.bilateral_mean(a8.astype(np.uint16),disk(10),s0=10,s1=10)
 
+
+    selem = np.ones((3,3))
+    selem[1,1] = 0
+    radius = 3
+    selem = disk(radius)
+    selem[radius,radius] = 0
+    print selem
+    noise = rank.noise_filter(a8,selem)
+    plt.imsave('noise.png',noise,cmap=plt.cm.gray)
+    plt.imsave('cam.png',a8,cmap=plt.cm.gray)
+    print noise
+
+    plt.figure()
+    plt.subplot(1,2,1)
+    plt.imshow(a8)
+    plt.subplot(1,2,2)
+    plt.imshow(noise)
+    plt.colorbar()
+    plt.show()
+
+
     plt.figure()
     plt.subplot(1,2,1)
     plt.imshow(den)
diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx
index 2f144e98..40ceecad 100644
--- a/skimage/filter/rank/rank.pyx
+++ b/skimage/filter/rank/rank.pyx
@@ -19,7 +19,7 @@ from skimage.filter.rank import _crank8, _crank16
 from skimage.filter.rank.generic import find_bitdepth
 
 __all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean', 'meansubstraction', 'median', 'minimum',
-           'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat']
+           'modal', 'morph_contr_enh', 'pop', 'threshold', 'tophat','noise_filter']
 
 
 def _apply(func8, func16, image, selem, out, mask, shift_x, shift_y):
@@ -580,3 +580,31 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
 
     return _apply(_crank8.tophat, _crank16.tophat, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
 
+def noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
+    """Return minimum absolute diffirence between a pixel and its neighborhood
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array (uint8 array or uint16). If image is uint16, the algorithm uses max. 12bit histogram,
+        an exception will be raised if image has a value > 4095
+    selem : ndarray
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+    out : ndarray
+        The array to store the result of the morphology. If None is
+        passed, a new array will be allocated.
+    mask : ndarray (uint8)
+        Mask array that defines (>0) area of the image included in the local neighborhood.
+        If None, the complete image is used (default).
+    shift_x, shift_y : (int)
+        Offset added to the structuring element center point.
+        Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
+
+    Returns
+    -------
+    out : uint8 array or uint16 array (same as input image)
+        The image noise .
+
+    """
+
+    return _apply(_crank8.noise_filter, None, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
\ No newline at end of file