Compressive sensing image reconstruction in aspect-limited data

The compressive sensing [1], [2] is an emerging technique for data acquisition and signal recovery which can be efficiently applied as an interesting solution method for tomographic problems, by virtue of its property of requiring lower dimensional data - with respect to that of solutions space - when finding the unknown, provided of course the latter admits a sparse representation in a certain basis. This communication deals with the solution of microwave imaging problems in aspect-limited data exploiting a Compressive Sensing (CS) based method. In particular, the inversion procedure was tested on the Contrast Source-Extended Born model and on the Born model.