diff --git a/starfish/image/_filter/__init__.py b/starfish/image/_filter/__init__.py
index 5b241b4df..5051914f1 100644
--- a/starfish/image/_filter/__init__.py
+++ b/starfish/image/_filter/__init__.py
@@ -9,6 +9,7 @@
 from . import clip
 from . import gaussian_high_pass
 from . import gaussian_low_pass
+from . import laplace
 from . import mean_high_pass
 from . import richardson_lucy_deconvolution
 from . import scale_by_percentile
diff --git a/starfish/image/_filter/laplace.py b/starfish/image/_filter/laplace.py
new file mode 100644
index 000000000..6395ea684
--- /dev/null
+++ b/starfish/image/_filter/laplace.py
@@ -0,0 +1,132 @@
+# Definition of the processing class
+import argparse
+from functools import partial
+from typing import Callable, Optional, Tuple, Union
+
+import numpy as np
+from scipy.ndimage import gaussian_laplace
+
+from starfish.image._filter._base import FilterAlgorithmBase
+from starfish.image._filter.util import (
+    determine_axes_to_split_by,
+    preserve_float_range,
+    validate_and_broadcast_kernel_size,
+)
+from starfish.imagestack.imagestack import ImageStack
+from starfish.types import Number
+
+
+class Laplace(FilterAlgorithmBase):
+    """
+    Multi-dimensional Laplace filter, using Gaussian second derivatives.
+    This filter wraps scipy.ndimage.gaussian_laplace
+
+    Parameters
+    ----------
+    sigma : Union[Number, Tuple[Number]]
+        Standard deviation for Gaussian kernel.
+    is_volume : bool
+        If True, 3d (z, y, x) volumes will be filtered, otherwise, filter 2d tiles
+        independently.
+
+    """
+
+    def __init__(
+            self,
+            sigma: Union[Number, Tuple[Number]], mode: str='reflect',
+            cval: float=0.0, is_volume: bool=False, **kwargs
+    ) -> None:
+        """Multi-dimensional gaussian-laplacian filter used to enhance dots against background
+
+        Parameters
+        ----------
+        sigma_gauss : Union[Number, Tuple[Number]]
+            Standard deviation for Gaussian kernel to enhance dots.
+
+        mode: The mode parameter determines how the input array is extended when
+            the filter overlaps a border. By passing a sequence of modes with
+            length equal to the number of dimensions of the input array,
+            different modes can be specified along each axis. Default value
+            is ‘reflect’.
+            The valid values and their behavior is as follows:
+
+            ‘reflect’ (d c b a | a b c d | d c b a)
+            The input is extended by reflecting about the edge of the last pixel.
+
+            ‘constant’ (k k k k | a b c d | k k k k)
+            The input is extended by filling all values beyond the edge with the same
+            constant value, defined by the cval parameter.
+
+            ‘nearest’ (a a a a | a b c d | d d d d)
+            The input is extended by replicating the last pixel.
+
+            ‘mirror’ (d c b | a b c d | c b a)
+            The input is extended by reflecting about the center of the last pixel.
+
+            ‘wrap’ (a b c d | a b c d | a b c d)
+            The input is extended by wrapping around to the opposite edge.
+
+        cval : scalar, optional
+        Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
+
+        is_volume: bool
+            True is the image is a stack
+        """
+
+        self.sigma = validate_and_broadcast_kernel_size(sigma, is_volume=is_volume)
+        self.mode = mode
+        self.cval = cval
+        self.is_volume = is_volume
+
+    _DEFAULT_TESTING_PARAMETERS = {"sigma": 0.5}
+
+    @classmethod
+    def _add_arguments(cls, group_parser: argparse.ArgumentParser) -> None:
+        group_parser.add_argument(
+            "--sigma", type=float,
+            help="Standard deviation of gaussian kernel for spot enhancement")
+        group_parser.add_argument(
+            "--mode", default="reflect",
+            help="How the input array is extended when the filter overlaps a border")
+        group_parser.add_argument(
+            "--cval", default=0.0,
+            help="Value to fill past edges of input if mode is ‘constant")
+        group_parser.add_argument(
+            "--is-volume", action="store_true",
+            help="indicates that the image stack should be filtered in 3d")
+
+    @staticmethod
+    def _gaussian_laplace(image: np.ndarray, sigma: Union[Number, Tuple[Number]],
+                          mode: str = 'reflect', cval: float = 0.0) -> np.ndarray:
+        filtered = gaussian_laplace(
+            image, sigma=sigma, mode=mode, cval=cval)
+
+        filtered = -filtered  # the peaks are negative so invert the signal
+        filtered = preserve_float_range(filtered)
+
+        return filtered
+
+    def run(
+            self, stack: ImageStack, in_place: bool = True, verbose: bool = True,
+            n_processes: Optional[int]=None
+    ) -> ImageStack:
+        """Perform filtering of an image stack
+        Parameters
+        ----------
+        stack : ImageStack
+            Stack to be filtered.
+        in_place : bool
+            if True, process ImageStack in-place, otherwise return a new stack
+        verbose : bool
+            if True, report on filtering progress (default = False)
+        Returns
+        -------
+        ImageStack :
+            if in-place is False, return the results of filter as a new stack
+        """
+        split_by = determine_axes_to_split_by(self.is_volume)
+        apply_filtering: Callable = partial(self._gaussian_laplace, sigma=self.sigma)
+        return stack.apply(
+            apply_filtering,
+            split_by=split_by, verbose=verbose, in_place=in_place, n_processes=n_processes,
+        )