  We report a fast, efficient and credible statistical reconstruction of any
two-phase patterns of islands of miscellaneous shapes and poly-dispersed in
sizes. In the proposed multi-scale approach called a weighted doubly-hybrid,
two different pairs of hybrid descriptors are used. As the first pair, we
employ entropic quantifiers, while correlation functions are the second pair.
Their competition allows considering a wider spectrum of morphological
features. Instead of a standard random initial configuration, a synthetic one
with the same number of islands as that of the target is created by a cellular
automaton. This is the key point for speeding-up of microstructure
reconstruction, making use of the simulated annealing technique. The program
procedure allows requiring the same values for the reconstructed and target
interface. The reconstruction terminates when three conditions related to the
accuracy, interface and number of islands are fulfilled. We verify the approach
on digitized images of a thin metallic film and a concrete sample
cross-section. For a given accuracy, our method significantly reduces the
number of accepted Monte Carlo steps when compared to the standard approach. At
the same time, it provides credible shapes and similar areas of islands,
keeping their number and the total interface of the target. To the best of our
knowledge, this is the first attempt to obtain such an outcome. The
cost-effective reconstructions suggest that the present technique could also be
used for patterns of islands with strongly jagged border lines.
