  We show that various systematics related to certain instrumental effects and
data reduction anomalies in wide field variability surveys can be efficiently
corrected by a Trend Filtering Algorithm (TFA) applied to the photometric time
series produced by standard data pipelines. Statistical tests, performed on the
database of the HATNet project, show that by the application of this filtering
method the cumulative detection probability of periodic transits increases by
up to 0.4 for variables brighter than 11 mag with a trend of increasing
efficiency toward brighter magnitudes. We also show that TFA can be used for
the reconstruction of periodic signals by iteratively filtering out systematic
distortions.
