diff --git a/examples/processing/background_subtraction.py b/examples/processing/background_subtraction.py
new file mode 100644
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+++ b/examples/processing/background_subtraction.py
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+"""
+Background subtraction
+==============
+
+If your diffraction data is noisy, you might want to subtract the background from the
+dataset. Pyxem offers some built-in functionality for this, with the
+`subtract_diffraction_background` class method. Custom filtering is also possible, an
+example is shown in the 'Filtering Data'-example.
+"""
+
+import pyxem as pxm
+import hyperspy.api as hs
+
+s = pxm.data.twinned_nanowire(allow_download=True)
+
+s_filtered = s.subtract_diffraction_background(
+    "difference of gaussians", 
+    inplace=False, 
+    min_sigma=2, 
+    max_sigma=8,
+    )
+
+hs.plot.plot_images(
+    [s.inav[9, 42], s_filtered.inav[9, 42]],
+    label=["Original", "Difference of Gaussians"],
+    tight_layout=True,
+    norm="symlog",
+    cmap="viridis",
+    colorbar=None,
+)
+# %%
+"""
+The available methods differ for `Diffraction2D` datasets and `PolarDiffraction2D`
+datasets.
+"""
+
+# Set the center of the diffraction pattern to its default,
+# i.e. the middle of the image
+s.calibrate.center = None
+
+# Transform to polar coordinates
+s_polar = s.get_azimuthal_integral2d(npt=100, mean=True)
+
+s_polar_filtered = s_polar.subtract_diffraction_background(
+    "radial median", 
+    inplace=False, 
+    )
+
+hs.plot.plot_images(
+    [s_polar.inav[9, 42], s_polar_filtered.inav[9, 42]],
+    label=["Original (polar)", "Radial Median"],
+    tight_layout=True,
+    norm="symlog",
+    cmap="viridis",
+    colorbar=None,
+)