  A multi-scale approach to the inverse reconstruction of a pattern's
microstructure is reported. Instead of a correlation function, a pair of
entropic descriptors (EDs) is proposed for stochastic optimization method. The
first of them measures a spatial inhomogeneity, for a binary pattern, or
compositional one, for a greyscale image. The second one quantifies a spatial
or compositional statistical complexity. The EDs reveal structural information
that is dissimilar, at least in part, to that given by correlation functions at
almost all of discrete length scales. The method is tested on a few digitized
binary and greyscale images. In each of the cases, the persuasive
reconstruction of the microstructure is found.
