diff --git a/connectomics/volume/processor/filters.py b/connectomics/volume/processor/filters.py
new file mode 100644
index 0000000..ec36c43
--- /dev/null
+++ b/connectomics/volume/processor/filters.py
@@ -0,0 +1,130 @@
+# coding=utf-8
+# Copyright 2023 The Google Research Authors.
+#
+# 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.
+"""Processors for filtering volumetric images."""
+
+from typing import Optional, Sequence
+
+from connectomics.volume import subvolume
+from connectomics.volume import subvolume_processor
+import numpy as np
+from scipy import ndimage
+
+
+SuggestedXyz = subvolume_processor.SuggestedXyz
+
+
+class HighPass(subvolume_processor.SubvolumeProcessor):
+  """Applies a high-pass filter to image data.
+
+  This is useful to make structures more visible at high zoom-out level.
+
+  The method implemented here was originally used by Yuelong Wu @ the Harvard
+  Lichtman lab for low-resolution visualization of the h01 dataset.
+  """
+
+  def process(self, subvol: subvolume.Subvolume) -> subvolume.SubvolumeOrMany:
+    box = subvol.bbox
+    input_ndarray = subvol.data
+    img = input_ndarray[0, 0, ...]
+    hp = img + ndimage.minimum_filter(255 - img, 3)
+    return self.crop_box_and_data(box, hp[np.newaxis, np.newaxis, ...])
+
+  def context(self):
+    return SuggestedXyz(3, 3, 0), SuggestedXyz(3, 3, 0)
+
+  def subvolume_size(self):
+    return SuggestedXyz(1024, 1024, 1)
+
+
+class ApplyFilter(subvolume_processor.SubvolumeProcessor):
+  """Applies an ndimage filter to the volume."""
+
+  def __init__(
+      self,
+      filter_size: int | Sequence[int],
+      filter_type: str,
+      mode: str,
+      dtype: Optional[str] = None,
+  ):
+    """Constructor.
+
+    Args:
+      filter_size: size of the filter kernel in pixels (int or [z]yx tuple)
+      filter_type: one of: median, max, min, uniform
+      mode: '2d' (filter will be applied to XY sections) or '3d'
+      dtype: dtype for computation and output data; defaults to same as input;
+        if specified, input data will be cast to this type prior to filtering
+    """
+    super().__init__()
+
+    filter_map = {
+        'median': ndimage.median_filter,
+        'max': ndimage.maximum_filter,
+        'min': ndimage.minimum_filter,
+        'uniform': ndimage.uniform_filter,
+    }
+    self._filter_type = filter_type
+    self._filter_fn = filter_map[filter_type]
+    self._mode = mode
+    if dtype is not None:
+      self._dtype = np.dtype(dtype).type
+    else:
+      self._dtype = None
+
+    if isinstance(filter_size, int):
+      s = filter_size
+      if self._mode == '2d':
+        self._filter_size = (1, s, s)
+      else:
+        self._filter_size = (s, s, s)
+    else:
+      if self._mode == '2d':
+        self._filter_size = (1, filter_size[0], filter_size[1])
+      else:
+        self._filter_size = filter_size
+
+  @property
+  def name_parts(self):
+    return (
+        self._filter_type,
+        'filter',
+        '_'.join(str(x) for x in self._filter_size),
+        self._mode,
+    )
+
+  def output_type(self, input_type):
+    if self._dtype is None:
+      return input_type
+    else:
+      return self._dtype
+
+  def context(self):
+    flt = np.array(self._filter_size[::-1], dtype=int)
+    pre, post = (flt // 2).tolist(), (flt - flt // 2).tolist()
+    if self._mode == '2d':
+      pre[-1] = post[-1] = 0
+    return pre, post
+
+  def process(self, subvol: subvolume.Subvolume) -> subvolume.SubvolumeOrMany:
+    box = subvol.bbox
+    input_ndarray = subvol.data
+    if input_ndarray.shape[0] != 1:
+      raise ValueError('Only 1-channel volumes are supported.')
+
+    out_dtype = self.output_type(input_ndarray.dtype)
+    input_ndarray = input_ndarray.astype(out_dtype)
+    out = np.zeros_like(input_ndarray)
+    out[0, ...] = self._filter_fn(input_ndarray[0, ...], self._filter_size)
+    return self.crop_box_and_data(box, out)
diff --git a/connectomics/volume/processor/filters_test.py b/connectomics/volume/processor/filters_test.py
new file mode 100644
index 0000000..c3519e1
--- /dev/null
+++ b/connectomics/volume/processor/filters_test.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+# Copyright 2023 The Google Research Authors.
+#
+# 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.
+"""Tests for the filters module."""
+
+from absl.testing import absltest
+from connectomics.volume.processor import filters
+
+import numpy as np
+
+
+class ApplyFilterTest(absltest.TestCase):
+
+  def test_context(self):
+    processor = filters.ApplyFilter(
+        (33, 44), 'max', '2d'
+    )
+    pre, post = processor.context()
+
+    np.testing.assert_array_equal(pre, [22, 16, 0])
+    np.testing.assert_array_equal(post, [22, 17, 0])
+
+    processor = filters.ApplyFilter(
+        (1, 33, 44), 'max', '3d'
+    )
+    pre, post = processor.context()
+
+    np.testing.assert_array_equal(pre, [22, 16, 0])
+    np.testing.assert_array_equal(post, [22, 17, 1])
+
+
+
+if __name__ == "__main__":
+  absltest.main()