  We present a new detection algorithm based on the wavelet transform for the
analysis of high energy astronomical images. The wavelet transform, due to its
multi-scale structure, is suited for the optimal detection of point-like as
well as extended sources, regardless of any loss of resolution with the
off-axis angle. Sources are detected as significant enhancements in the wavelet
space, after the subtraction of the non-flat components of the background.
Detection thresholds are computed through Monte Carlo simulations in order to
establish the expected number of spurious sources per field. The source
characterization is performed through a multi-source fitting in the wavelet
space. The procedure is designed to correctly deal with very crowded fields,
allowing for the simultaneous characterization of nearby sources. To obtain a
fast and reliable estimate of the source parameters and related errors, we
apply a novel decimation technique which, taking into account the correlation
properties of the wavelet transform, extracts a subset of almost independent
coefficients. We test the performance of this algorithm on synthetic fields,
analyzing with particular care the characterization of sources in poor
background situations, where the assumption of Gaussian statistics does not
hold. For these cases, where standard wavelet algorithms generally provide
underestimated errors, we infer errors through a procedure which relies on
robust basic statistics. Our algorithm is well suited for the analysis of
images taken with the new generation of X-ray instruments equipped with CCD
technology which will produce images with very low background and/or high
source density.
