Compressed sensing (CS) is an innovative technique allowing to representsignals through a small number of their linear projections. Hence, CS can bethought of as a natural candidate for acquisition of multidimensional signals,as the amount of data acquired and processed by conventional sensors couldcreate problems in terms of computational complexity. In this paper, we proposea framework for the acquisition and reconstruction of multidimensionalcorrelated signals. The approach is general and can be applied to D dimensionalsignals, even if the algorithms we propose to practically implement sucharchitectures apply to 2-D and 3-D signals. The proposed architectures employiterative local signal reconstruction based on a hybrid transform/predictioncorrelation model, coupled with a proper initialization strategy.
展开▼
