diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index b972bb2b7c6..3c874313b93 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -1099,3 +1099,155 @@ def __call__(self, img):
 
     def __repr__(self):
         return self.__class__.__name__ + '(p={0})'.format(self.p)
+
+
+class HistogramTransform(object):
+    """
+    Transforms the distribution of the input tensor to match that
+    of the list of template histograms corresponding to each channel.
+
+    A template historgram must be set initially.
+    Args:
+        tensor (numpy.ndarray):
+            Image to transform; the histogram is computed over the flattened
+            array
+        noise_range (float): Default is 0. A uniform noise ranged between
+            (-noise_range, +noise_range) will be added to pixels randomly.
+    Returns:
+        histogram transformed tensor:
+            The output tensor type matches the input, either numpy.ndarray
+            or torch.Tensor.
+    """
+
+    def __init__(self, template_histograms):
+        '''
+        Args:
+            template_histograms:
+                A list of template histograms.
+                Each template histogram must consist of the tuple
+                (counts (numpy.ndarray), bins (numpy.ndarray)).
+                template_histograms is a list of numpy.histogram outputs,
+                each corresponding to each channel of the input tensor
+                to be transformed.
+                If 1 channel, still feed as a list, i.e. [(counts, bin)].
+                Example:
+                Assuming img is made by ToTensor(some pil image)
+                and has 3 (RGB) channels, one can get the histogram as such:
+                histR = np.histogram(img[0].numpy().ravel(),
+                    bins=256, range=[0, 1])
+                histG = np.histogram(img[1].numpy().ravel(),
+                    bins=256, range=[0, 1])
+                histB = np.histogram(img[2].numpy().ravel(),
+                    bins=256, range=[0, 1])
+        '''
+
+        self.template_histograms = template_histograms
+        self.num_channels = len(template_histograms)
+
+    def __call__(self, tensor, noise_range=0, dtype=torch.float32):
+        """
+        Transforms the distribution of the input tensor to match that
+        of the template histogram. If a list of histograms is provided
+        and it maches the number of channels of the input tensor, each
+        channel will be transformed with the corresponding histogram.
+
+        This funciton utilises histogram_tranform_1D for an easier user
+        interface.
+
+        Args:
+            tensor (numpy.ndarray):
+                Image to transform; the histogram is computed over the
+                flattened array for each channel.
+            noise_range (float): Default is 0. A uniform noise ranged between
+                (-noise_range, +noise_range) will be added to pixels randomly.
+        Returns:
+            histogram transformed tensor:
+                The output tensor type matches the input, either numpy.ndarray
+                or torch.Tensor.
+        """
+
+        tensorType = type(tensor)
+        tensor = np.asanyarray(tensor)
+
+        channels = []
+        for (c, templateHisto) in enumerate(self.template_histograms):
+            channels.append(self.histogram_transform_1D(tensor[c],
+                            templateHisto, noise_range=noise_range))
+
+        transformed_tensor = np.asanyarray(channels)
+
+        # Convert to the original type
+
+        if tensorType == torch.Tensor:
+            transformed_tensor = torch.tensor(transformed_tensor, dtype=dtype)
+
+        return transformed_tensor
+
+    # Core of the computation to be used by histogram_transform method internally
+
+    def histogram_transform_1D(self, tensor, template_histogram, noise_range=0):
+        """
+        Transforms the distribution of the input tensor to match that
+        of the template histogram.
+
+        Input tensor will be flattened, transformed, and rearranged
+        to the original shape.
+        Mainly intended for call by class functions.
+
+        Args:
+            tensor (numpy.ndarray): Image to transform; the histogram is
+            computed over the flattened array.
+            template_histogram (tubple of (numpy.ndarray, numpy.ndarray)):
+                The template histogram consisiting of a tuple of (counts, bins).
+                See (the output of) numpy.histogram.
+            noise_range (float): Default is 0. A uniform noise ranged between
+                (-noise_range, +noise_range) will be added to pixels randomly.
+        Returns:
+            histogram transformed array (numpy.ndarray):
+                The transformed output tensor/image that maches the input
+        """
+
+        # === Template Histogram ===
+        # t_... stands for template_
+
+        (t_counts, t_bins) = template_histogram
+
+        # t_bin_idx not required
+
+        # Take the cumsum of the counts and normalize by the number of pixels to
+        # Get the empirical cumulative distribution functions
+        # (maps value --> quantile)
+
+        t_quantiles = np.cumsum(t_counts).astype(np.float32)
+        t_quantiles /= t_quantiles[-1]
+
+        # === Input Tensor ===
+        # Convert to flattened numpy array
+
+        tensor = np.asanyarray(tensor)
+        originalShape = tensor.shape
+        tensor = tensor.ravel()
+
+        # Get counts, bins, and corresponding bin indices for each tensor value
+
+        (counts, bins) = np.histogram(tensor, bins=t_bins)
+        bin_idx = np.searchsorted(t_bins[:-2], tensor)
+
+        # See comments for t_quantiles
+
+        quantiles = np.cumsum(counts).astype(np.float32)
+        quantiles /= quantiles[-1]
+
+        # === Histogram Transformation ===
+        # interpolate linearly to find the pixel values in the template image
+        # that corresponds most closely to the quantiles for the input tensor
+
+        interp_t_values = np.interp(quantiles, t_quantiles, t_bins[:-1])
+        tensor_transformed = interp_t_values[bin_idx]
+
+        noise = np.random.uniform(low=-noise_range, high=+noise_range,
+                                  size=len(tensor_transformed))
+        tensor_transformed += noise
+        tensor_transformed = np.maximum(tensor_transformed, min(t_bins))
+
+        return tensor_transformed.reshape(originalShape)