diff --git a/tensorflow_addons/image/BUILD b/tensorflow_addons/image/BUILD
index a1a09d66bf..fd5577e213 100644
--- a/tensorflow_addons/image/BUILD
+++ b/tensorflow_addons/image/BUILD
@@ -18,6 +18,11 @@ py_library(
         "connected_components.py",
         "resampler_ops.py",
         "compose_ops.py",
+        "solarize_ops.py",
+        "posterize_ops.py",
+        "auto_contrast_ops.py",
+        "to_grayscale_ops.py",
+        "color_jitter_ops.py",
     ]),
     data = [
         ":sparse_image_warp_test_data",
@@ -177,3 +182,63 @@ py_test(
         ":image",
     ],
 )
+
+py_test(
+    name = "solarize_ops_test",
+    size = "medium",
+    srcs = [
+        "solarize_ops_test.py",
+    ],
+    main = "solarize_ops_test.py",
+    deps = [
+        ":image",
+    ],
+)
+
+py_test(
+    name = "posterize_ops_test",
+    size = "medium",
+    srcs = [
+        "posterize_ops_test.py",
+    ],
+    main = "posterize_ops_test.py",
+    deps = [
+        ":image",
+    ],
+)
+
+py_test(
+    name = "auto_contrast_ops_test",
+    size = "medium",
+    srcs = [
+        "auto_contrast_ops_test.py",
+    ],
+    main = "auto_contrast_ops_test.py",
+    deps = [
+        ":image",
+    ],
+)
+
+py_test(
+    name = "to_grayscale_ops_test",
+    size = "medium",
+    srcs = [
+        "to_grayscale_ops_test.py",
+    ],
+    main = "to_grayscale_ops_test.py",
+    deps = [
+        ":image",
+    ],
+)
+
+py_test(
+    name = "color_jitter_ops",
+    size = "medium",
+    srcs = [
+        "color_jitter_ops.py",
+    ],
+    main = "color_jitter_ops.py",
+    deps = [
+        ":image",
+    ],
+)
diff --git a/tensorflow_addons/image/__init__.py b/tensorflow_addons/image/__init__.py
index fbd5cda029..efa01bde3c 100644
--- a/tensorflow_addons/image/__init__.py
+++ b/tensorflow_addons/image/__init__.py
@@ -29,3 +29,9 @@
 from tensorflow_addons.image.transform_ops import transform
 from tensorflow_addons.image.translate_ops import translate
 from tensorflow_addons.image.compose_ops import blend
+from tensorflow_addons.image.solarize_ops import solarize
+from tensorflow_addons.image.solarize_ops import solarize_add
+from tensorflow_addons.image.posterize_ops import posterize
+from tensorflow_addons.image.auto_contrast_ops import autocontrast
+from tensorflow_addons.image.to_grayscale_ops import to_grayscale
+from tensorflow_addons.image.color_jitter_ops import color_jitter
diff --git a/tensorflow_addons/image/auto_contrast_ops.py b/tensorflow_addons/image/auto_contrast_ops.py
new file mode 100644
index 0000000000..674d02e5a8
--- /dev/null
+++ b/tensorflow_addons/image/auto_contrast_ops.py
@@ -0,0 +1,60 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Maximize (normalize) image contrast.
+This function calculates a histogram of the input image,
+removes cutoff percent of the lightest and darkest pixels
+ from the histogram, and remaps the image so
+that the darkest pixel becomes black (0),
+and the lightest becomes white (255). """
+
+import tensorflow as tf
+from tensorflow_addons.utils.types import TensorLike
+
+
+def autocontrast(image: TensorLike) -> TensorLike:
+    """Implements Autocontrast function from PIL using TF ops.
+  Args:
+    image: A 3D uint8 tensor.
+  Returns:
+    The image after it has had autocontrast applied to it and will be of type
+    uint8.
+  """
+
+    def scale_channel(image: TensorLike) -> TensorLike:
+        """Scale the 2D image using the autocontrast rule."""
+        # A possibly cheaper version can be done using cumsum/unique_with_counts
+        # over the histogram values, rather than iterating over the entire image.
+        # to compute mins and maxes.
+        lo = tf.cast(tf.reduce_min(image), dtype=tf.float32)
+        hi = tf.cast(tf.reduce_max(image), dtype=tf.float32)
+
+        # Scale the image, making the lowest value 0 and the highest value 255.
+        def scale_values(im: TensorLike) -> TensorLike:
+            scale = 255.0 / (hi - lo)
+            offset = -lo * scale
+            im = tf.cast(im, dtype=tf.float32) * scale + offset
+            im = tf.clip_by_value(im, 0.0, 255.0)
+            return tf.cast(im, tf.uint8)
+
+        result = tf.cond(hi > lo, lambda: scale_values(image), lambda: image)
+        return result
+
+    # Assumes RGB for now.  Scales each channel independently
+    # and then stacks the result.
+    s1 = scale_channel(image[:, :, 0])
+    s2 = scale_channel(image[:, :, 1])
+    s3 = scale_channel(image[:, :, 2])
+    image = tf.stack([s1, s2, s3], 2)
+    return image
diff --git a/tensorflow_addons/image/auto_contrast_ops_test.py b/tensorflow_addons/image/auto_contrast_ops_test.py
new file mode 100644
index 0000000000..6ac2ec2f8f
--- /dev/null
+++ b/tensorflow_addons/image/auto_contrast_ops_test.py
@@ -0,0 +1,39 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Test auto_contrast_ops """
+import sys
+import pytest
+import tensorflow as tf
+from tensorflow_addons.image import auto_contrast_ops
+from tensorflow_addons.utils import test_utils
+from absl.testing import parameterized
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class AutoContrastTest(tf.test.TestCase, parameterized.TestCase):
+    """AutoContrastTest class to test the working of
+    methods images"""
+
+    def test_contrast(self):
+        """ Method to test the auto_contrast technique on images """
+        if tf.executing_eagerly():
+            image = tf.constant([[1, 1], [1, 1]], dtype=tf.uint8)
+            stacked_img = tf.stack([image] * 3, 2)
+            contrast_image = auto_contrast_ops.autocontrast(stacked_img)
+            self.assertAllEqual(tf.shape(contrast_image), tf.shape(stacked_img))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))
diff --git a/tensorflow_addons/image/color_jitter_ops.py b/tensorflow_addons/image/color_jitter_ops.py
new file mode 100644
index 0000000000..9b4e67df7e
--- /dev/null
+++ b/tensorflow_addons/image/color_jitter_ops.py
@@ -0,0 +1,142 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" This method is used to distorts the color of the image """
+import tensorflow as tf
+from tensorflow_addons.utils.types import TensorLike
+
+
+def color_jitter(
+    image: TensorLike, strength: float, random_order: bool = True
+) -> TensorLike:
+    """Distorts the color of the image.
+  Args:
+    image: The input image tensor.
+    strength: the floating number for the strength of the color augmentation <= 2.0.
+    random_order: A bool, specifying whether to randomize the jittering order.
+  Returns:
+    The distorted image tensor.
+  """
+    image = tf.cast(image, dtype=tf.dtypes.float32)
+    brightness = 0.8 * strength
+    contrast = 0.8 * strength
+    saturation = 0.8 * strength
+    hue = 0.2 * strength
+    if random_order:
+        return color_jitter_rand(image, brightness, contrast, saturation, hue)
+    else:
+        return color_jitter_nonrand(image, brightness, contrast, saturation, hue)
+
+
+def color_jitter_nonrand(
+    image: TensorLike,
+    brightness: float = 0,
+    contrast: float = 0,
+    saturation: float = 0,
+    hue: float = 0,
+) -> TensorLike:
+    """Distorts the color of the image (jittering order is fixed).
+  Args:
+    image: The input image tensor.
+    brightness: A float, specifying the brightness for color jitter.
+    contrast: A float, specifying the contrast for color jitter.
+    saturation: A float, specifying the saturation for color jitter.
+    hue: A float, specifying the hue for color jitter.
+  Returns:
+    The distorted image tensor.
+  """
+    with tf.name_scope("distort_color"):
+
+        def apply_transform(i, x, brightness, contrast, saturation, hue):
+            """Apply the i-th transformation."""
+            if brightness != 0 and i == 0:
+                x = tf.image.random_brightness(x, max_delta=brightness)
+            elif contrast != 0 and i == 1:
+                x = tf.image.random_contrast(x, lower=1 - contrast, upper=1 + contrast)
+            elif saturation != 0 and i == 2:
+                x = tf.image.random_saturation(
+                    x, lower=1 - saturation, upper=1 + saturation
+                )
+            elif hue != 0:
+                x = tf.image.random_hue(x, max_delta=hue)
+            return x
+
+        for i in range(4):
+            image = apply_transform(i, image, brightness, contrast, saturation, hue)
+            image = tf.clip_by_value(image, 0.0, 1.0)
+        return image
+
+
+def color_jitter_rand(
+    image: TensorLike,
+    brightness: float = 0,
+    contrast: float = 0,
+    saturation: float = 0,
+    hue: float = 0,
+) -> TensorLike:
+    """Distorts the color of the image (jittering order is random).
+  Args:
+    image: The input image tensor.
+    brightness: A float, specifying the brightness for color jitter.
+    contrast: A float, specifying the contrast for color jitter.
+    saturation: A float, specifying the saturation for color jitter.
+    hue: A float, specifying the hue for color jitter.
+  Returns:
+    The distorted image tensor.
+  """
+    with tf.name_scope("distort_color"):
+
+        def apply_transform(i, x):
+            """Apply the i-th transformation."""
+
+            def brightness_foo():
+                if brightness == 0:
+                    return x
+                else:
+                    return tf.image.random_brightness(x, max_delta=brightness)
+
+            def contrast_foo():
+                if contrast == 0:
+                    return x
+                else:
+                    return tf.image.random_contrast(
+                        x, lower=tf.math.abs(1 - contrast), upper=1 + contrast
+                    )
+
+            def saturation_foo():
+                if saturation == 0:
+                    return x
+                else:
+                    return tf.image.random_saturation(
+                        x, lower=tf.math.abs(1 - saturation), upper=1 + saturation
+                    )
+
+            def hue_foo():
+                if hue == 0:
+                    return x
+                else:
+                    return tf.image.random_hue(x, max_delta=hue)
+
+            x = tf.cond(
+                tf.less(i, 2),
+                lambda: tf.cond(tf.less(i, 1), brightness_foo, contrast_foo),
+                lambda: tf.cond(tf.less(i, 3), saturation_foo, hue_foo),
+            )
+            return x
+
+        perm = tf.random.shuffle(tf.range(4))
+        for i in range(4):
+            image = apply_transform(perm[i], image)
+            image = tf.clip_by_value(image, 0.0, 1.0)
+        return tf.cast(image, dtype=tf.uint8)
diff --git a/tensorflow_addons/image/color_jitter_ops_test.py b/tensorflow_addons/image/color_jitter_ops_test.py
new file mode 100644
index 0000000000..68d6c77c31
--- /dev/null
+++ b/tensorflow_addons/image/color_jitter_ops_test.py
@@ -0,0 +1,41 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Test of color_jitter method"""
+
+import sys
+import pytest
+import tensorflow as tf
+from tensorflow_addons.image import color_jitter_ops
+from tensorflow_addons.utils import test_utils
+from absl.testing import parameterized
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class ColorJitterTest(tf.test.TestCase, parameterized.TestCase):
+    """ColorJitterTest class to test the color distortion image operation"""
+
+    def test_color_jitter(self):
+        """ Method to test the color distortion technique on images """
+        if tf.executing_eagerly():
+            image = tf.constant([[1, 2], [5, 3]], dtype=tf.uint8)
+            stacked_img = tf.stack([image] * 3, 2)
+            strength = 0.3
+            jitter_image = color_jitter_ops.color_jitter(stacked_img, strength)
+            self.assertAllEqual(tf.shape(jitter_image), tf.shape(stacked_img))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))
diff --git a/tensorflow_addons/image/posterize_ops.py b/tensorflow_addons/image/posterize_ops.py
new file mode 100644
index 0000000000..0b83df757f
--- /dev/null
+++ b/tensorflow_addons/image/posterize_ops.py
@@ -0,0 +1,32 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" Posterize method used to reduce the
+    number of bits foreach color channel"""
+
+import tensorflow as tf
+from tensorflow_addons.utils.types import TensorLike
+
+
+def posterize(image: TensorLike, bits: int) -> TensorLike:
+    """Reduce the number of bits for each color channel.
+    Args:
+      image: The image to posterize
+      bits: The number of bits to keep for each channel(1-8)
+
+    Returns:
+      An image
+    """
+    shift = 8 - bits
+    return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
diff --git a/tensorflow_addons/image/posterize_ops_test.py b/tensorflow_addons/image/posterize_ops_test.py
new file mode 100644
index 0000000000..18c9de62f4
--- /dev/null
+++ b/tensorflow_addons/image/posterize_ops_test.py
@@ -0,0 +1,40 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test of solarize_ops"""
+
+import sys
+import pytest
+import tensorflow as tf
+from tensorflow_addons.image import posterize_ops
+from tensorflow_addons.utils import test_utils
+from absl.testing import parameterized
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class PosterizeOpsTest(tf.test.TestCase, parameterized.TestCase):
+    """PosterizeOpsTest class to test the working of
+    methods images"""
+
+    def test_posterize(self):
+        """ Method to test the posterize technique on images """
+        if tf.executing_eagerly():
+            image = tf.constant(tf.ones([4, 4], dtype=tf.dtypes.uint8)) * 255
+            bits = 2
+            posterize_image = posterize_ops.posterize(image, bits)
+            self.assertAllEqual(tf.shape(image), tf.shape(posterize_image))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))
diff --git a/tensorflow_addons/image/solarize_ops.py b/tensorflow_addons/image/solarize_ops.py
new file mode 100644
index 0000000000..7eaec699d6
--- /dev/null
+++ b/tensorflow_addons/image/solarize_ops.py
@@ -0,0 +1,54 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" This module is used to invert all pixel values above a threshold
+   which simply means segmentation. """
+
+import tensorflow as tf
+from tensorflow_addons.utils.types import TensorLike
+
+
+def solarize(image: TensorLike, threshold: float = 128) -> TensorLike:
+
+    """Method to solarize the image
+  image: input image
+  threshold: threshold value to solarize the image
+
+  Returns:
+    A solarized image
+  """
+    # For each pixel in the image, select the pixel
+    # if the value is less than the threshold.
+    # Otherwise, subtract 255 from the pixel.
+    return tf.where(image < threshold, image, 255 - image)
+
+
+def solarize_add(
+    image: TensorLike, addition: int = 0, threshold: float = 128
+) -> TensorLike:
+    """Method to add solarize to the image
+  image: input image
+  addition: addition amount to add in image
+  threshold: threshold value to solarize the image
+
+  Returns:
+    Solarized image with addition values
+  """
+    # For each pixel in the image less than threshold
+    # we add 'addition' amount to it and then clip the
+    # pixel value to be between 0 and 255. The value
+    # of 'addition' is between -128 and 128.
+    added_image = tf.cast(image, tf.int64) + addition
+    added_image = tf.cast(tf.clip_by_value(added_image, 0, 255), tf.uint8)
+    return tf.where(image < threshold, added_image, image)
diff --git a/tensorflow_addons/image/solarize_ops_test.py b/tensorflow_addons/image/solarize_ops_test.py
new file mode 100644
index 0000000000..1c3c9ed230
--- /dev/null
+++ b/tensorflow_addons/image/solarize_ops_test.py
@@ -0,0 +1,38 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test of solarize_ops"""
+
+import sys
+import pytest
+import tensorflow as tf
+from tensorflow_addons.image import solarize_ops
+from tensorflow_addons.utils import test_utils
+from absl.testing import parameterized
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class SolarizeOPSTest(tf.test.TestCase, parameterized.TestCase):
+    """SolarizeOPSTest class to test the solarize images"""
+
+    def test_solarize(self):
+        if tf.executing_eagerly():
+            image2 = tf.constant(tf.ones([4, 4], dtype=tf.uint8)) * 255
+            threshold = 10
+            solarize_img = solarize_ops.solarize(image2, threshold)
+            self.assertAllEqual(tf.shape(solarize_img), tf.shape(image2))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))
diff --git a/tensorflow_addons/image/to_grayscale_ops.py b/tensorflow_addons/image/to_grayscale_ops.py
new file mode 100644
index 0000000000..cd70612dea
--- /dev/null
+++ b/tensorflow_addons/image/to_grayscale_ops.py
@@ -0,0 +1,33 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" method to convert the color images into grayscale
+by keeping the channel same"""
+import tensorflow as tf
+from tensorflow_addons.utils.types import TensorLike
+
+
+def to_grayscale(image: TensorLike, keep_channels: bool = True) -> TensorLike:
+    """ Method to convert the color image into grayscale
+    by keeping the channels same.
+
+    Args:
+      image: color image to convert into grayscale
+      keep_channels: boolean parameter for channels
+    Returns:
+      Image"""
+    image = tf.image.rgb_to_grayscale(image)
+    if keep_channels:
+        image = tf.tile(image, [1, 1, 3])
+    return image
diff --git a/tensorflow_addons/image/to_grayscale_ops_test.py b/tensorflow_addons/image/to_grayscale_ops_test.py
new file mode 100644
index 0000000000..a82a3f71e2
--- /dev/null
+++ b/tensorflow_addons/image/to_grayscale_ops_test.py
@@ -0,0 +1,39 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Test of grayscale method"""
+
+import sys
+import pytest
+import tensorflow as tf
+from tensorflow_addons.image import to_grayscale_ops
+from tensorflow_addons.utils import test_utils
+from absl.testing import parameterized
+
+
+@test_utils.run_all_in_graph_and_eager_modes
+class ToGrayScaleOpsTest(tf.test.TestCase, parameterized.TestCase):
+    """ToGrayScaleOpsTest class to test the grayscale image operation"""
+
+    def test_grayscale(self):
+        """ Method to test the grayscale technique on images """
+        if tf.executing_eagerly():
+            image = tf.constant([[1, 2], [5, 3]], dtype=tf.uint8)
+            stacked_img = tf.stack([image] * 3, 2)
+            grayscale_image = to_grayscale_ops.to_grayscale(stacked_img)
+            self.assertAllEqual(tf.shape(grayscale_image), tf.shape(stacked_img))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))