diff --git a/starfish/core/image/Filter/__init__.py b/starfish/core/image/Filter/__init__.py
index 8e5365a66..2a62ecac3 100644
--- a/starfish/core/image/Filter/__init__.py
+++ b/starfish/core/image/Filter/__init__.py
@@ -8,6 +8,7 @@
 from .gaussian_low_pass import GaussianLowPass
 from .laplace import Laplace
 from .linear_unmixing import LinearUnmixing
+from .map import Map
 from .match_histograms import MatchHistograms
 from .max_proj import MaxProject
 from .mean_high_pass import MeanHighPass
diff --git a/starfish/core/image/Filter/map.py b/starfish/core/image/Filter/map.py
new file mode 100644
index 000000000..04a470103
--- /dev/null
+++ b/starfish/core/image/Filter/map.py
@@ -0,0 +1,178 @@
+import importlib
+from enum import Enum
+from typing import (
+    Callable,
+    cast,
+    Mapping,
+    Optional,
+    Set,
+    Union
+)
+
+from starfish.core.imagestack.imagestack import ImageStack
+from starfish.core.types import Axes, Clip
+from ._base import FilterAlgorithmBase
+
+
+class Map(FilterAlgorithmBase):
+    """
+    Map from input to output by applying a specified function to the input.  The output must have
+    the same shape as the input.
+
+    Parameters
+    ----------
+    func : str
+        Name of a function in the module specified by the ``module`` parameter to apply across the
+        dimension(s) specified by dims.  The function is resolved by ``getattr(<module>, func)``,
+        except in the cases of predefined aliases.  See :py:class:`FunctionSource` for more
+        information about aliases.
+    module : FunctionSource
+        Python module that serves as the source of the function.  It must be listed as one of the
+        members of :py:class:`FunctionSource`.
+
+        Currently, the supported FunctionSources are:
+        - ``np``: the top-level package of numpy
+        - ``scipy``: the top-level package of scipy
+    in_place : bool
+        Execute the operation in-place.  (default: False)
+    group_by : Set[Axes]
+        Axes to split the data along.  For example, splitting a 2D array (axes: X, Y; size: 3, 4)
+        by X results in 3 calls to the method, each with arrays of size 4.  (default {Axes.ROUND,
+        Axes.CH, Axes.ZPLANE})
+    clip_method : Union[str, Clip]
+        (Default Clip.CLIP) Controls the way that data are scaled to retain skimage dtype
+        requirements that float data fall in [0, 1].
+        Clip.CLIP: data above 1 are set to 1, and below 0 are set to 0
+        Clip.SCALE_BY_IMAGE: data above 1 are scaled by the maximum value, with the maximum
+        value calculated over the entire ImageStack
+        Clip.SCALE_BY_CHUNK: data above 1 are scaled by the maximum value, with the maximum
+        value calculated over each slice, where slice shapes are determined by the group_by
+        parameters.
+
+    Examples
+    --------
+    Applying a divide function.
+        >>> from starfish.core.imagestack.test.factories.synthetic_stack import synthetic_stack
+        >>> from starfish.image import Filter
+        >>> from starfish.types import Axes
+        >>> stack = synthetic_stack()
+        >>> divider = Filter.Map("divide", 4)
+        >>> by_four = divider.run(stack)
+
+    See Also
+    --------
+    starfish.core.types.Axes
+
+    """
+
+    class FunctionSource(Enum):
+        """Each FunctionSource declares a package from which reduction methods can be obtained.
+        Generally, the packages should be those that are included as starfish's dependencies for
+        reproducibility.
+
+        Many packages are broken into subpackages which are not necessarily implicitly imported when
+        importing the top-level package.  For example, ``scipy.linalg`` is not implicitly imported
+        when one imports ``scipy``.  To avoid the complexity of enumerating each scipy subpackage in
+        FunctionSource, we assemble the fully-qualified method name, and then try all the
+        permutations of how one could import that method.
+
+        In the example of ``scipy.linalg.norm``, we try the following:
+
+        1. import ``scipy``, attempt to resolve ``linalg.norm``.
+        2. import ``scipy.linalg``, attempt to resolve ``norm``.
+        """
+
+        def __init__(self, top_level_package: str, aliases: Optional[Mapping[str, str]] = None):
+            self.top_level_package = top_level_package
+            self.aliases = aliases or {}
+
+        def _resolve_method(self, method: str) -> Callable:
+            """Resolve a method.  The method itself might be enclosed in a package, such as
+            subpackage.actual_method.  In that case, we will need to attempt to resolve it in the
+            following sequence:
+
+            1. import top_level_package, then try to resolve subpackage.actual_method recursively
+               through ``getattr`` calls.
+            2. import top_level_package.subpackage, then try to resolve actual_method through
+               ``gettatr`` calls.
+
+            This is done instead of just creating a bunch of FunctionSource for libraries that have
+            a lot of packages that are not implicitly imported by importing the top-level package.
+            """
+            # first resolve the aliases.
+            actual_method = self.aliases.get(method, method)
+
+            method_splitted = actual_method.split(".")
+            splitted = [self.top_level_package]
+            splitted.extend(method_splitted)
+
+            for divider in range(1, len(splitted)):
+                import_section = splitted[:divider]
+                getattr_section = splitted[divider:]
+
+                imported = importlib.import_module(".".join(import_section))
+
+                try:
+                    for getattr_name in getattr_section:
+                        imported = getattr(imported, getattr_name)
+                    return cast(Callable, imported)
+                except AttributeError:
+                    pass
+
+            raise AttributeError(
+                f"Unable to resolve the method {actual_method} from package "
+                f"{self.top_level_package}")
+
+        np = ("numpy",)
+        """Function source for the numpy libraries"""
+        scipy = ("scipy",)
+
+    def __init__(
+        self,
+            func: str,
+            *func_args,
+            module: FunctionSource = FunctionSource.np,
+            in_place: bool = False,
+            group_by: Optional[Set[Union[Axes, str]]] = None,
+            clip_method: Clip = Clip.CLIP,
+            **func_kwargs,
+    ) -> None:
+        self.func = module._resolve_method(func)
+        self.in_place = in_place
+        if group_by is None:
+            group_by = {Axes.ROUND, Axes.CH, Axes.ZPLANE}
+        self.group_by: Set[Axes] = {Axes(axis) for axis in group_by}
+        self.clip_method = clip_method
+        self.func_args = func_args
+        self.func_kwargs = func_kwargs
+
+    _DEFAULT_TESTING_PARAMETERS = {"dims": ['r'], "func": 'max'}
+
+    def run(
+            self,
+            stack: ImageStack,
+            *args,
+    ) -> Optional[ImageStack]:
+        """Map from input to output by applying a specified function to the input.
+
+        Parameters
+        ----------
+        stack : ImageStack
+            Stack to be filtered.
+
+        Returns
+        -------
+        Optional[ImageStack] :
+            If in-place is False, return the results of filter as a new stack.  Otherwise return
+            None
+
+        """
+
+        # Apply the reducing function
+        return stack.apply(
+            self.func,
+            *self.func_args,
+            group_by=self.group_by,
+            in_place=self.in_place,
+            clip_method=self.clip_method,
+            **self.func_kwargs)
diff --git a/starfish/core/image/Filter/test/test_map.py b/starfish/core/image/Filter/test/test_map.py
new file mode 100644
index 000000000..16923b96a
--- /dev/null
+++ b/starfish/core/image/Filter/test/test_map.py
@@ -0,0 +1,11 @@
+from starfish.core.imagestack.test.factories import synthetic_stack
+from .. import Map
+
+
+def test_map():
+    """test that apply correctly applies a simple function across 2d tiles of a Stack"""
+    stack = synthetic_stack()
+    assert (stack.xarray == 1).all()
+    mapper = Map("divide", 2)
+    output = mapper.run(stack)
+    assert (output.xarray == 0.5).all()