  The Square Kilometre Array and its pathfinders ASKAP and MeerKAT will produce
prodigious amounts of data that necessitate automated source finding. The
performance of automated source finders can be improved by pre-processing a
dataset. In preparation for the WALLABY and DINGO surveys, we have used a test
HI datacube constructed from actual Westerbork Telescope noise and WHISP HI
galaxies to test the real world improvement of linear smoothing, the {\sc
Duchamp} source finder's wavelet de-noising, iterative median smoothing and
mathematical morphology subtraction, on intensity threshold source finding of
spectral line datasets. To compare these pre-processing methods we have
generated completeness-reliability performance curves for each method and a
range of input parameters. We find that iterative median smoothing produces the
best source finding results for ASKAP HI spectral line observations, but
wavelet de-noising is a safer pre-processing technique.
  In this paper we also present our implementations of iterative median
smoothing and mathematical morphology subtraction.
