diff --git a/python/lsst/meas/algorithms/computeExPsf.py b/python/lsst/meas/algorithms/computeExPsf.py
new file mode 100644
index 000000000..572b7c180
--- /dev/null
+++ b/python/lsst/meas/algorithms/computeExPsf.py
@@ -0,0 +1,120 @@
+# This file is part of meas_algorithms.
+#
+# Developed for the LSST Data Management System.
+# This product includes software developed by the LSST Project
+# (https://www.lsst.org).
+# See the COPYRIGHT file at the top-level directory of this distribution
+# for details of code ownership.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+
+from lsst.meas.algorithms.treecorrUtils import TreecorrConfig
+from lsst.pex.config import Config, ConfigField
+from lsst.pipe.base import Task
+import lsst.pipe.base as pipeBase
+import treecorr
+import copy
+import numpy.typing as npt
+
+
+__all__ = ("ComputeExPsfTask", "ComputeExPsfConfig")
+
+
+class ComputeExPsfConfig(Config):
+    """Config class of ComputeExPsfTask."""
+
+    treecorr = ConfigField(
+        dtype=TreecorrConfig,
+        doc="treecorr config.",
+    )
+
+
+class ComputeExPsfTask(Task):
+    """Compute Ex for PSF.
+
+    Compute scalar correlation function from
+    PSF ellipticity residuals to compute TEx
+    metrics.
+
+    Parameters
+    ----------
+    de1: `np.ndarray`
+        PSF ellipticity residuals component 1.
+    de2: `np.ndarray`
+        PSF ellipticity residuals component 2.
+    ra: `np.ndarray`
+        Right ascension coordinate.
+    dec: `np.ndarray`
+        Declination coordinate.
+    units: `str`
+        In which units are ra and dec. units supported
+        are the same as the one in treecorr.
+
+    Returns
+    -------
+    struct : `lsst.pipe.base.Struct`
+            The struct contains the following data:
+        ``E1``: `float`
+            <de1 de1> scalar correlation function, compute
+            in an angular bin define in TreecorrConfig.
+        ``E2``: `float`
+            <de2 de2> scalar correlation function, compute
+            in an angular bin define in TreecorrConfig.
+        ``Ex``: `float`
+            <de1 de2> scalar cross-correlation function, compute
+            in an angular bin define in TreecorrConfig.
+    """
+
+    ConfigClass = ComputeExPsfConfig
+    _DefaultName = "computeExPsf"
+
+    def run(
+        self,
+        de1: npt.NDArray,
+        de2: npt.NDArray,
+        ra: npt.NDArray,
+        dec: npt.NDArray,
+        units: str = "arcmin",
+    ) -> pipeBase.Struct:
+
+        if units != self.config.treecorr.sep_units:
+            raise ValueError(
+                "units from ComputeExPsfConfig and"
+                "ComputeExPsfTask are not the same (%s != %s)"
+                % ((units, self.config.treecorr.sep_units))
+            )
+
+        kwargs_cat = {
+            "ra": ra,
+            "dec": dec,
+            "ra_units": units,
+            "dec_units": units,
+        }
+
+        cat1 = treecorr.Catalog(k=de1, **kwargs_cat)
+        cat2 = treecorr.Catalog(k=de2, **kwargs_cat)
+
+        config_kk = self.config.treecorr.toDict()
+
+        kk = treecorr.KKCorrelation(config_kk)
+
+        kk.process(cat1)
+        kk_E1 = copy.deepcopy(kk.xi[0])
+        kk.process(cat2)
+        kk_E2 = copy.deepcopy(kk.xi[0])
+        kk.process(cat1, cat2)
+        kk_Ex = copy.deepcopy(kk.xi[0])
+
+        return pipeBase.Struct(metric_E1=kk_E1, metric_E2=kk_E2, metric_Ex=kk_Ex)
diff --git a/tests/test_computeExPsf.py b/tests/test_computeExPsf.py
new file mode 100644
index 000000000..9f17ebe4b
--- /dev/null
+++ b/tests/test_computeExPsf.py
@@ -0,0 +1,289 @@
+# This file is part of meas_algorithms.
+#
+# Developed for the LSST Data Management System.
+# This product includes software developed by the LSST Project
+# (https://www.lsst.org).
+# See the COPYRIGHT file at the top-level directory of this distribution
+# for details of code ownership.
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+
+import unittest
+from typing import Optional
+import numpy.typing as npt
+
+import numpy as np
+
+import lsst.utils.tests
+from lsst.meas.algorithms import GaussianProcessTreegp
+from lsst.meas.algorithms.computeExPsf import ComputeExPsfTask
+import lsst.pipe.base as pipeBase
+
+
+def rbf_kernel(
+    x1: npt.NDArray, x2: npt.NDArray, sigma: float, correlation_length: float
+) -> npt.NDArray:
+    """
+    Computes the radial basis function (RBF) kernel matrix.
+
+    Parameters:
+    -----------
+    x1 : `np.array`
+        Location of training data point with shape (n_samples, n_features).
+    x2 : `np.array`
+        Location of training/test data point with shape (n_samples, n_features).
+    sigma : `float`
+        The scale parameter of the kernel.
+    correlation_length : `float`
+        The correlation length parameter of the kernel.
+
+    Returns:
+    --------
+    kernel : `np.ndarray`
+        RBF kernel matrix with shape (n_samples, n_samples).
+    """
+    distance_squared = np.sum((x1[:, None, :] - x2[None, :, :]) ** 2, axis=-1)
+    kernel = (sigma**2) * np.exp(-0.5 * distance_squared / (correlation_length**2))
+    return kernel
+
+
+def generate_gaussian_random_field(
+    xmin: int = 0,
+    xmax: int = 2000,
+    ymin: int = 0,
+    ymax: int = 2000,
+    npoints: int = 10000,
+    nmax: int = 1000,
+    std: float = 1.0,
+    correlation_length: float = 10.0,
+    white_noise: float = 1.0,
+    seed: int = 42,
+    input_coord: Optional[npt.NDArray] = None,
+) -> pipeBase.Struct:
+    """Generate a Gaussian Random Field.
+
+    Function to generate a Gaussian Random Field.
+    Help for unit test and generate spatial correlated
+    function and have an analytical 2-point correlation
+    to compared with (Gaussian here).
+
+    Parameters
+    ----------
+    xmin: `int`
+        Min value in x direction.
+        Default: ``0``
+    xmax: `int`
+        Max value in x direction.
+        Default: ``2000``
+    ymin: `int`
+        Min value in y direction.
+        Default: ``0``
+    ymax: `int`
+        Max value in y direction.
+        Default: ``2000``
+    npoints: `int`
+        Number of data points generated.
+        Default: ``10000``
+    nmax: `int`
+        Max number of data points generated using
+        `np.random.multivariate_normal`. If
+        npoints>nmax, a GP will be used in addition.
+        Default: ``1000``
+    std: `float`
+        Amplitude of the gaussian random field.
+        Default: ``1.0``
+    correlation_length: `float`
+        Correlation length of the gaussian random field.
+        Default: ``10.0``
+    white_noise: `float`
+        Noise added to the gaussian random field.
+        Default: ``1.0``
+    seed: `int`
+        Seed of the random generator.
+        Default: ``42``
+    input_coord: `np.ndarray`
+        Take a input coord to generate the Gaussian Random field
+        Default: ``None``
+
+    Returns
+    -------
+    struct : `lsst.pipe.base.Struct`
+            The struct contains the following data:
+        ``coord``: `np.ndarray`
+            2D coordinate of the gaussian random field
+        ``z``: `np.ndarray`
+            Scalar value of the gaussian random field
+    """
+    np.random.seed(seed)
+
+    if input_coord is not None:
+        npoints = len(input_coord[:, 0])
+
+    if npoints > nmax:
+        ngenerated = nmax
+    else:
+        ngenerated = npoints
+
+    if input_coord is None or npoints > nmax:
+        x1 = np.random.uniform(xmin, xmax, ngenerated)
+        x2 = np.random.uniform(ymin, ymax, ngenerated)
+        coord1 = np.array([x1, x2]).T
+    else:
+        coord1 = input_coord
+    kernel = rbf_kernel(coord1, coord1, std, correlation_length)
+    kernel += np.eye(ngenerated) * white_noise**2
+
+    z1 = np.random.multivariate_normal(np.zeros(ngenerated), kernel)
+
+    # Data augmentation. Create a gaussian random field
+    # with npoints>nmax is to slow. So generate nmax points
+    # and then interpolate it with a GP to do data augmentation.
+
+    if npoints > nmax:
+        if input_coord is None:
+            x1 = np.random.uniform(xmin, xmax, npoints)
+            x2 = np.random.uniform(ymin, ymax, npoints)
+            coord = np.array([x1, x2]).T
+        else:
+            coord = input_coord
+
+        tgp = GaussianProcessTreegp(
+            std=std,
+            correlation_length=correlation_length,
+            white_noise=white_noise,
+            mean=0.0,
+        )
+        tgp.fit(coord1, z1)
+        z = tgp.predict(coord)
+    else:
+        coord = coord1
+        z = z1
+
+    return pipeBase.Struct(coord=coord, z=z)
+
+
+class ComputeExPsfTestCase(lsst.utils.tests.TestCase):
+    """Test ComputeExPsfTask."""
+
+    def setUp(self) -> None:
+        super().setUp()
+
+        output1 = generate_gaussian_random_field(
+            xmin=0,
+            xmax=2000,
+            ymin=0,
+            ymax=2000,
+            npoints=10000,
+            nmax=2000,
+            std=1.0,
+            correlation_length=200.0,
+            white_noise=0.01,
+            seed=42,
+            input_coord=None,
+        )
+
+        output2 = generate_gaussian_random_field(
+            xmin=0,
+            xmax=2000,
+            ymin=0,
+            ymax=2000,
+            npoints=10000,
+            nmax=2000,
+            std=1.0,
+            correlation_length=100.0,
+            white_noise=0.01,
+            seed=44,
+            input_coord=output1.coord,
+        )
+
+        self.coord1 = output1.coord
+        self.coord2 = output2.coord
+        self.de1 = output1.z
+        self.de2 = output2.z
+
+    def test_comp_ex_psf(self) -> None:
+        """Test that ex metric are compute and make sense."""
+
+        np.testing.assert_equal(self.coord1, self.coord2)
+
+        ra = self.coord1[:, 0]
+        dec = self.coord1[:, 1]
+
+        config = ComputeExPsfTask.ConfigClass()
+
+        config.treecorr.min_sep = 0.01
+        config.treecorr.max_sep = 5.0
+        config.treecorr.nbins = 1
+        config.treecorr.bin_type = "Linear"
+        config.treecorr.sep_units = "arcmin"
+
+        task = ComputeExPsfTask(config)
+        output1 = task.run(self.de1, self.de2, ra, dec, units="arcmin")
+
+        # At small scale, expect the scalar two-point correlation function
+        # to be close to the input variance for de1 and de2. Cross correlation
+        # between de1 and de2 should be zeros are they are 2 indendant field.
+
+        np.testing.assert_allclose(output1.metric_E1, 1.0, atol=2e-1)
+        np.testing.assert_allclose(output1.metric_E2, 1.0, atol=2e-1)
+        np.testing.assert_allclose(output1.metric_Ex, 0.0, atol=1e-1)
+
+        config = ComputeExPsfTask.ConfigClass()
+
+        config.treecorr.min_sep = 5.0
+        config.treecorr.max_sep = 600.0
+        config.treecorr.nbins = 1
+        config.treecorr.bin_type = "Linear"
+        config.treecorr.sep_units = "arcmin"
+
+        # At intermediar scale, expect E1>E2>Ex.
+
+        task = ComputeExPsfTask(config)
+        output2 = task.run(self.de1, self.de2, ra, dec, units="arcmin")
+
+        np.testing.assert_allclose(output2.metric_E1, 0.20, atol=1e-1)
+        np.testing.assert_allclose(output2.metric_E2, 0.05, atol=1e-1)
+        np.testing.assert_allclose(output2.metric_Ex, 0.0, atol=1e-1)
+
+        config = ComputeExPsfTask.ConfigClass()
+
+        config.treecorr.min_sep = 600.0
+        config.treecorr.max_sep = 1000.0
+        config.treecorr.nbins = 1
+        config.treecorr.bin_type = "Linear"
+        config.treecorr.sep_units = "arcmin"
+
+        # At large scale, expect the scalar two-point correlation function to
+        # be all close to 0.
+
+        task = ComputeExPsfTask(config)
+        output2 = task.run(self.de1, self.de2, ra, dec, units="arcmin")
+
+        np.testing.assert_allclose(output2.metric_E1, 0.0, atol=1e-1)
+        np.testing.assert_allclose(output2.metric_E2, 0.0, atol=1e-1)
+        np.testing.assert_allclose(output2.metric_Ex, 0.0, atol=1e-1)
+
+
+def setup_module(module):
+    lsst.utils.tests.init()
+
+
+class MemoryTestCase(lsst.utils.tests.MemoryTestCase):
+    pass
+
+
+if __name__ == "__main__":
+    lsst.utils.tests.init()
+    unittest.main()