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diff --git a/src/silx/gui/plot/tools/compare/core.py b/src/silx/gui/plot/tools/compare/core.py
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+# /*##########################################################################
+#
+# Copyright (c) 2018-2019 European Synchrotron Radiation Facility
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# ###########################################################################*/
+"""This module provides main objects shared by the compare image plot.
+"""
+
+__authors__ = ["V. Valls"]
+__license__ = "MIT"
+__date__ = "09/06/2023"
+
+
+import numpy
+import enum
+import contextlib
+from typing import NamedTuple
+
+from silx.gui.plot.items.image import ImageBase
+from silx.gui.plot.items.core import ItemChangedType, ColormapMixIn
+
+from silx.opencl import ocl
+
+if ocl is not None:
+    try:
+        from silx.opencl import sift
+    except ImportError:
+        # sift module is not available (e.g., in official Debian packages)
+        sift = None
+else:  # No OpenCL device or no pyopencl
+    sift = None
+
+
+@enum.unique
+class VisualizationMode(enum.Enum):
+    """Enum for each visualization mode available."""
+
+    ONLY_A = "a"
+    ONLY_B = "b"
+    VERTICAL_LINE = "vline"
+    HORIZONTAL_LINE = "hline"
+    COMPOSITE_RED_BLUE_GRAY = "rbgchannel"
+    COMPOSITE_RED_BLUE_GRAY_NEG = "rbgnegchannel"
+    COMPOSITE_A_MINUS_B = "aminusb"
+
+
+@enum.unique
+class AlignmentMode(enum.Enum):
+    """Enum for each alignment mode available."""
+
+    ORIGIN = "origin"
+    CENTER = "center"
+    STRETCH = "stretch"
+    AUTO = "auto"
+
+
+class AffineTransformation(NamedTuple):
+    """Description of a 2D affine transformation: translation, scale and
+    rotation.
+    """
+
+    tx: float
+    ty: float
+    sx: float
+    sy: float
+    rot: float
+
+
+class _CompareImageItem(ImageBase, ColormapMixIn):
+    """Description of a virtual item of images to compare, in order to share
+    the data through the silx components.
+    """
+
+    def __init__(self):
+        ImageBase.__init__(self)
+        ColormapMixIn.__init__(self)
+        self.__image1 = None
+        self.__image2 = None
+        self.__vizualisationMode = VisualizationMode.ONLY_A
+
+    def getImageData1(self):
+        return self.__image1
+
+    def getImageData2(self):
+        return self.__image2
+
+    def setImageData1(self, image1):
+        if self.__image1 is image1:
+            return
+        self.__image1 = image1
+        self._updated(ItemChangedType.DATA)
+
+    def setImageData2(self, image2):
+        if self.__image2 is image2:
+            return
+        self.__image2 = image2
+        self._updated(ItemChangedType.DATA)
+
+    def getVizualisationMode(self) -> VisualizationMode:
+        return self.__vizualisationMode
+
+    @contextlib.contextmanager
+    def _updateColormapRange(self, previousMode, mode):
+        """COMPOSITE_A_MINUS_B don't have the same data range than others.
+
+        If the colormap is using a fixed range, it is updated in order to set
+        a similar range with the new data.
+        """
+        normalize_colormap = (
+            previousMode == VisualizationMode.COMPOSITE_A_MINUS_B
+            or mode == VisualizationMode.COMPOSITE_A_MINUS_B
+        )
+        if normalize_colormap:
+            data = self._getConcatenatedData(copy=False)
+            if data is None or data.size == 0:
+                normalize_colormap = False
+            else:
+                std1 = numpy.nanstd(data)
+                mean1 = numpy.nanmean(data)
+        yield
+
+        def transfer(v, std1, mean1, std2, mean2):
+            """Transfer a value from a data range to another using statistics"""
+            if v is None:
+                return None
+            rv = (v - mean1) / std1
+            return rv * std2 + mean2
+
+        if normalize_colormap:
+            data = self._getConcatenatedData(copy=False)
+            if data is not None and data.size != 0:
+                std2 = numpy.nanstd(data)
+                mean2 = numpy.nanmean(data)
+                c = self.getColormap()
+                if c is not None:
+                    vmin, vmax = c.getVRange()
+                    vmin = transfer(vmin, std1, mean1, std2, mean2)
+                    vmax = transfer(vmax, std1, mean1, std2, mean2)
+                    c.setVRange(vmin, vmax)
+
+    def setVizualisationMode(self, mode: VisualizationMode):
+        if self.__vizualisationMode == mode:
+            return None
+        with self._updateColormapRange(self.__vizualisationMode, mode):
+            self.__vizualisationMode = mode
+        self._updated(ItemChangedType.DATA)
+
+    def _getConcatenatedData(self, copy=True):
+        if self.__image1 is None and self.__image2 is None:
+            return None
+        if self.__image1 is None:
+            return numpy.array(self.__image2, copy=copy)
+        if self.__image2 is None:
+            return numpy.array(self.__image1, copy=copy)
+
+        if self.__vizualisationMode == VisualizationMode.COMPOSITE_A_MINUS_B:
+            # In this case the histogram have to be special
+            if self.__image1.shape == self.__image2.shape:
+                return self.__image1.astype(numpy.float32) - self.__image2.astype(
+                    numpy.float32
+                )
+        else:
+            d1 = self.__image1[numpy.isfinite(self.__image1)]
+            d2 = self.__image2[numpy.isfinite(self.__image2)]
+            return numpy.concatenate((d1, d2))
+
+    def _updated(self, event=None, checkVisibility=True):
+        # Synchronizes colormapped data if changed
+        if event in (ItemChangedType.DATA, ItemChangedType.MASK):
+            data = self._getConcatenatedData(copy=False)
+            return self._setColormappedData(data, copy=False)
+        super()._updated(event=event, checkVisibility=checkVisibility)
+
+    def getColormappedData(self, copy=True):
+        """
+        Reimplementation of the `ColormapMixIn.getColormappedData` method.
+
+        This is used to provide a consistent auto scale on the compared images.
+        """
+        return self._getConcatenatedData(copy=copy)