  We present a progressive image decomposition method based on a novel
non-linear filter named Sub-window Variance filter. Our method is specifically
designed for image detail enhancement purpose; this application requires
extraction of image details which are small in terms of both spatial and
variation scales. We propose a local statistical edge model which develops its
edge awareness using spatially defined image statistics. Our decomposition
method is controlled by two intuitive parameters which allow the users to
define what image details to suppress or enhance. By using the summed-area
table acceleration method, our decomposition pipeline is highly parallel. The
proposed filter is gradient preserving and this allows our enhancement results
free from the gradient-reversal artefact. In our evaluations, we compare our
method in various multi-scale image detail manipulation applications with other
mainstream solutions.
