diff --git a/skimage/exposure/_adapthist.py b/skimage/exposure/_adapthist.py
index 7d79dc5e..9f7cadf9 100644
--- a/skimage/exposure/_adapthist.py
+++ b/skimage/exposure/_adapthist.py
@@ -20,15 +20,23 @@ from .. import img_as_float, img_as_uint
 from ..color.adapt_rgb import adapt_rgb, hsv_value
 from ..exposure import rescale_intensity
 from ..util import view_as_windows
+<<<<<<< HEAD
 from .._shared.utils import skimage_deprecation, warnings
+=======
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
 
 
 NR_OF_GREY = 2 ** 14  # number of grayscale levels to use in CLAHE algorithm
 
 
 @adapt_rgb(hsv_value)
+<<<<<<< HEAD
 def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
                        nbins=256, kernel_size=None):
+=======
+def equalize_adapthist(image, kernel_size=64, ntiles_x=None, ntiles_y=None,
+        clip_limit=0.01, nbins=256):
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
     """Contrast Limited Adaptive Histogram Equalization (CLAHE).
 
     An algorithm for local contrast enhancement, that uses histograms computed
@@ -42,11 +50,19 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
     kernel_size: integer or 2-tuple
         Defines the shape of contextual regions used in the algorithm.
         If an integer is given, the shape will be a square of
+<<<<<<< HEAD
         sidelength given by this value.
     ntiles_x : int, optional (deprecated in favor of ``kernel_size``)
         Number of tile regions in the X direction (horizontal).
     ntiles_y : int, optional (deprecated if favor of ``kernel_size``)
         Number of tile regions in the Y direction (vertical).
+=======
+        sidelength given by its value.
+    ntiles_x : int, optional
+        Number of tile regions in the X direction.
+    ntiles_y : int, optional
+        Number of tile regions in the Y direction.
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
     clip_limit : float: optional
         Clipping limit, normalized between 0 and 1 (higher values give more
         contrast).
@@ -77,6 +93,7 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
     """
     image = img_as_uint(image)
     image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
+<<<<<<< HEAD
     if kernel_size is None:
         warnings.warn('`ntiles_*` have been deprecated in favor of '
                       '`kernel_size`.  The `ntiles_*` keyword arguments '
@@ -85,6 +102,12 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
         ntiles_y = ntiles_y or 8
         kernel_size = (np.round(image.shape[0] / ntiles_y),
                        np.round(image.shape[1] / ntiles_x))
+=======
+    if ntiles_x or ntiles_y:
+        ntiles_x = ntiles_x or 8
+        ntiles_y = ntiles_y or 8
+        kernel_size = (image.shape[0] / ntiles_y, image.shape[1] / ntiles_x)
+>>>>>>> 3bcbbc0... Update equalize_adapthist to use new view_as_windows
 
     if isinstance(kernel_size, numbers.Number):
         kernel_size = (kernel_size, kernel_size)
diff --git a/skimage/exposure/tests/test_exposure.py b/skimage/exposure/tests/test_exposure.py
index 6b2d0a2e..3d684445 100644
--- a/skimage/exposure/tests/test_exposure.py
+++ b/skimage/exposure/tests/test_exposure.py
@@ -211,17 +211,14 @@ def test_adapthist_grayscale():
     img = skimage.img_as_float(data.astronaut())
     img = rgb2gray(img)
     img = np.dstack((img, img, img))
-    with expected_warnings(['precision loss|non-contiguous input', 'deprecated']):
+    with expected_warnings(['precision loss|non-contiguous input', 
+                            'deprecated']):
         adapted_old = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01,
-                                              nbins=128)
-        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01,
-                                              nbins=128)
-    np.testing.assert_allclose(adapted, adapted_old)
+        adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01, nbins=128)
     assert_almost_equal = np.testing.assert_almost_equal
     assert img.shape == adapted.shape
     assert_almost_equal(peak_snr(img, adapted), 90.669, 3)
     assert_almost_equal(norm_brightness_err(img, adapted), 0.084, 3)
-
     return data, adapted