diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
index 0617fc7681bd..9df73aa9902f 100644
--- a/lib/matplotlib/axes/_base.py
+++ b/lib/matplotlib/axes/_base.py
@@ -3073,7 +3073,7 @@ def draw(self, renderer):
         if not self.figure.canvas.is_saving():
             artists = [
                 a for a in artists
-                if not a.get_animated() or isinstance(a, mimage.AxesImage)]
+                if not a.get_animated()]
         artists = sorted(artists, key=attrgetter('zorder'))
 
         # rasterize artists with negative zorder
diff --git a/lib/matplotlib/pyplot.py b/lib/matplotlib/pyplot.py
index c5044fe7242f..e0e8a3260785 100644
--- a/lib/matplotlib/pyplot.py
+++ b/lib/matplotlib/pyplot.py
@@ -2438,10 +2438,11 @@ def matshow(A: ArrayLike, fignum: None | int = None, **kwargs) -> AxesImage:
     """
     Display an array as a matrix in a new figure window.
 
-    The origin is set at the upper left hand corner and rows (first
-    dimension of the array) are displayed horizontally.  The aspect
-    ratio of the figure window is that of the array, unless this would
-    make an excessively short or narrow figure.
+    The origin is set at the upper left hand corner.
+    The first dimension of the array represents the length of the vertical column
+    the second dimension of the array represents the length of the
+    horizontal column. The aspect ratio of the figure window is that of the array,
+    unless this would make an excessively short or narrow figure.
 
     Tick labels for the xaxis are placed on top.
 
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 8c43b04e0eb7..394359d1b6ce 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -8914,3 +8914,10 @@ def test_axhvlinespan_interpolation():
     ax.axhline(1, c="C0", alpha=.5)
     ax.axhspan(.8, .9, fc="C1", alpha=.5)
     ax.axhspan(.6, .7, .8, .9, fc="C2", alpha=.5)
+
+
+@check_figures_equal(extensions=["png"])
+def test_anim_without_image(fig_test, fig_ref):
+    ax_ref = fig_ref.subplots()
+    imdata = np.random.random((20, 20))
+    ax_ref.plot(imdata, animated=True)