diff --git a/celldetection/models/filters.py b/celldetection/models/filters.py
index f6ca960..219bc38 100644
--- a/celldetection/models/filters.py
+++ b/celldetection/models/filters.py
@@ -6,7 +6,7 @@
 from typing import Union
 from torch.nn.common_types import _size_2_t
 
-__all__ = ['Filter2d', 'PascalFilter2d', 'ScharrFilter2d']
+__all__ = ['Filter2d', 'PascalFilter2d', 'ScharrFilter2d', 'SobelFilter2d']
 
 
 class Filter2d(nn.Conv2d):
@@ -34,7 +34,7 @@ def __init__(
             ...     [1, 0, -1],
             ... ], dtype=torch.float32)
             ... sobel_layer = Filter2d(in_channels=3, kernel=sobel, padding=1, trainable=False)
-            ... sobel_layer
+            ... sobel_layer, sobel_layer.weight
             (Filter2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3, bias=False),
              tensor([[ 1.,  0., -1.],
                      [ 2.,  0., -2.],
@@ -182,3 +182,52 @@ def get_kernel2d(transpose=False):
         if transpose:
             kernel = kernel.T
         return kernel
+
+
+class SobelFilter2d(Filter2d):
+    def __init__(
+            self,
+            in_channels: int,
+            stride: _size_2_t = 1,
+            padding: Union[str, _size_2_t] = 0,
+            dilation: _size_2_t = 1,
+            padding_mode: str = 'zeros',
+            device=None,
+            dtype=None,
+            odd_padding=True,
+            trainable=False,
+            transpose=False
+    ) -> None:
+        """Sobel Filter 2d.
+
+        Applies the 3x3 Sobel image gradient operator.
+
+        References:
+            - https://en.wikipedia.org/wiki/Sobel_operator
+
+        Args:
+            in_channels: Number of input channels.
+            stride: Stride.
+            padding: Padding.
+            dilation: Spacing between kernel elements.
+            padding_mode: One of ``'zeros'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'zeros'``.
+            device: Device.
+            dtype: Data type.
+            odd_padding: Whether to apply one-sided padding to account for even kernel sizes.
+            trainable: Whether the kernel should be trainable.
+            transpose: ``False`` for :math:`h_x` kernel, ``True`` for :math:`h_y` kernel.
+        """
+        super().__init__(in_channels=in_channels, kernel=self.get_kernel2d(transpose),
+                         stride=stride, padding=padding, dilation=dilation, odd_padding=odd_padding,
+                         trainable=trainable, padding_mode=padding_mode, device=device, dtype=dtype)
+
+    @staticmethod
+    def get_kernel2d(transpose=False):
+        sobel = torch.as_tensor([
+            [1, 0, -1],
+            [2, 0, -2],
+            [1, 0, -1.],
+        ])
+        if transpose:
+            sobel = sobel.T
+        return sobel