  Compressed sensing (CS) is an innovative technique allowing to represent
signals through a small number of their linear projections. Hence, CS can be
thought of as a natural candidate for acquisition of multidimensional signals,
as the amount of data acquired and processed by conventional sensors could
create problems in terms of computational complexity. In this paper, we propose
a framework for the acquisition and reconstruction of multidimensional
correlated signals. The approach is general and can be applied to D dimensional
signals, even if the algorithms we propose to practically implement such
architectures apply to 2-D and 3-D signals. The proposed architectures employ
iterative local signal reconstruction based on a hybrid transform/prediction
correlation model, coupled with a proper initialization strategy.
