diff --git a/python/lsst/pipe/tasks/prettyPictureMaker/_task.py b/python/lsst/pipe/tasks/prettyPictureMaker/_task.py
index e87f14460..c01f66fdf 100644
--- a/python/lsst/pipe/tasks/prettyPictureMaker/_task.py
+++ b/python/lsst/pipe/tasks/prettyPictureMaker/_task.py
@@ -22,6 +22,7 @@
 from __future__ import annotations
 
 __all__ = (
+    "ChannelRGBConfig",
     "PrettyPictureTask",
     "PrettyPictureConnections",
     "PrettyPictureConfig",
@@ -40,9 +41,8 @@
 from typing import TYPE_CHECKING, cast, Any
 from lsst.skymap import BaseSkyMap
 
-from scipy.stats import norm
+from scipy.stats import norm, halfnorm, mode
 from scipy.ndimage import binary_dilation
-from scipy.optimize import minimize
 from scipy.interpolate import RBFInterpolator
 from skimage.restoration import inpaint_biharmonic
 
@@ -84,7 +84,7 @@ class PrettyPictureConnections(
             "Model of the static sky, used to find temporal artifacts. Typically a PSF-Matched, "
             "sigma-clipped coadd. Written if and only if assembleStaticSkyModel.doWrite=True"
         ),
-        name="{coaddTypeName}CoaddPsfMatched",
+        name="pretty_coadd",
         storageClass="ExposureF",
         dimensions=("tract", "patch", "skymap", "band"),
         multiple=True,
@@ -252,6 +252,17 @@ class PrettyPictureConfig(PipelineTaskConfig, pipelineConnections=PrettyPictureC
             "inpaint": "Use surrounding pixels to determine likely value",
         },
     )
+    recenterNoise = Field[float](
+        doc="Recenter the noise away from zero. Supplied value is in units of sigma",
+        optional=True,
+        default=None,
+    )
+    noiseSearchThreshold = Field[float](
+        doc=(
+            "Flux threshold below which most flux will be considered noise, used to estimate noise properties"
+        ),
+        default=10,
+    )
 
     def setDefaults(self):
         self.channelConfig["i"] = ChannelRGBConfig(r=1, g=0, b=0)
@@ -268,6 +279,92 @@ class PrettyPictureTask(PipelineTask):
 
     config: ConfigClass
 
+    def _find_normal_stats(self, array):
+        """Calculate standard deviation from negative values using half-normal distribution.
+
+        Parameters
+        ----------
+        array : `numpy.array`
+            Input array of numerical values.
+
+        Returns
+        -------
+        mean : `float`
+            The central moment of the distribution
+        sigma : `float`
+            Estimated standard deviation from negative values. Returns np.inf if:
+            - No negative values exist in the array
+            - Half-normal fitting fails
+        """
+        # Extract negative values efficiently
+        values_noise = array[array < self.config.noiseSearchThreshold]
+
+        # find the mode
+        center = mode(np.round(values_noise, 2)).mode
+
+        # extract the negative values
+        values_neg = array[array < center]
+
+        # Return infinity if no negative values found
+        if values_neg.size == 0:
+            return 0, np.inf
+
+        try:
+            # Fit half-normal distribution to absolute negative values
+            mu, sigma = halfnorm.fit(np.abs(values_neg))
+        except (ValueError, RuntimeError):
+            # Handle fitting failures (e.g., constant data, optimization issues)
+            return 0, np.inf
+
+        return center, sigma
+
+    def _match_sigmas_and_recenter(self, *arrays, factor=1):
+        """Scale array values to match minimum standard deviation across arrays
+        and recenter noise.
+
+        Adjusts values below each array's sigma by scaling and shifting them to
+        align with the minimum sigma value across all input arrays. This operates
+        in-place for efficiency.
+
+        Parameters
+        ----------
+        *arrays : any number of `numpy.array`
+            Variable number of input arrays to process.
+        factor : float, optional
+            Scaling factor for adjustments (default: 1).
+
+        """
+        # Calculate standard deviations for all arrays
+        sigmas = []
+        mus = []
+        for arr in arrays:
+            m, s = self._find_normal_stats(arr)
+            mus.append(m)
+            sigmas.append(s)
+        mus = np.array(mus)
+        sigmas = np.array(sigmas)
+
+        # If no sigmas could be determined, return the original
+        # arrays.
+        if not np.any(np.isfinite(sigmas)):
+            return
+
+        min_sig = np.min(sigmas)
+
+        for mu, sigma, array in zip(mus, sigmas, arrays):
+            # Identify values below the array's sigma threshold
+            lower_pos = (array - mu) < sigma
+
+            # Skip processing if sigma is invalid
+            if not np.isfinite(sigma):
+                continue
+
+            # Calculate scaling ratio relative to minimum sigma
+            sigma_ratio = min_sig / sigma
+
+            # Apply adjustment to qualifying values
+            array[lower_pos] = (array[lower_pos] - mu) * sigma_ratio + min_sig * factor
+
     def run(self, images: Mapping[str, Exposure]) -> Struct:
         """Turns the input arguments in arguments into an RGB array.
 
@@ -337,6 +434,11 @@ def run(self, images: Mapping[str, Exposure]) -> Struct:
         except Exception:
             psf = None
 
+        if self.config.recenterNoise:
+            self._match_sigmas_and_recenter(
+                imageRArray, imageGArray, imageBArray, factor=self.config.recenterNoise
+            )
+
         # assert for typing reasons
         assert jointMask is not None
         # Run any image level correction plugins
@@ -635,13 +737,8 @@ def fixBackground(self, image):
         if np.any(mask):
             # use a minimizer to determine best mu and sigma for a Gaussian
             # given only samples below the mean of the Gaussian.
-            result = minimize(
-                self._neg_log_likelihood,
-                (maxLikely, initial_std),
-                args=(image[mask]),
-                bounds=((maxLikely, None), (1e-8, None)),
-            )
-            mu_hat, sigma_hat = result.x
+            _, sigma_hat = halfnorm.fit(np.abs(image[mask] - maxLikely))
+            mu_hat = maxLikely
         else:
             mu_hat, sigma_hat = (maxLikely, 2 * initial_std)