The statistical characterization of realistic, complex backgrounds where targets may be embedded is essential in the optimization of methods for target acquisition. A modeling framework for complex backgrounds that yields perceptually realistic images may provide a path towards such essential characterizations. To this end, we developed a framework for the synthesis of statistical textured backgrounds. The results of the syntheses of three rock-like structures and that of grass indicate some of the capabilities of the framework. We further extended the methods to synthesize biological tissue samples which present two forms of background complexity: a slowly, spatially varying meanbackground and a residual texture image. The extended framework allows synthesis of each component independently. A mathematical phantom for modeling inhomogeneous backgrounds is then proposed. First and second order statistics of various textures are then presented and a measure of distance between two images is proposed. Finally we discuss how such a framework may lead to effective statistical descriptions of such complex backgrounds for target acquisition.