  Events in the solar corona are often widely separated in their timescales,
which can allow them to be identified when they would otherwise be confused
with emission from other sources in the corona. Methods for cleanly separating
such events based on their timescales are thus desirable for research in the
field. This paper develops a technique for identifying time-varying signals in
solar coronal image sequences which is based on a per-pixel running median
filter and an understanding of photon-counting statistics. Example applications
to 'EIT Waves' and small-scale dynamics are shown, both using data from the 193
Angstrom channel on AIA. The technique is found to discriminate EIT Waves more
cleanly than the running and base difference techniques most commonly used. It
is also demonstrated that there is more signal in the data than is commonly
appreciated, finding that the waves can be traced to the edge of the AIA field
of view when the data are rebinned to increase the signal-to-noise ratio.
