  An intriguing phenomenon in many instances of compressed sensing is that the
reconstruction quality is governed not just by the overall sparsity of the
signal, but also on its structure. This paper is about understanding this
phenomenon, and demonstrating how it can be fruitfully exploited by the design
of suitable sampling strategies in order to outperform more standard compressed
sensing techniques based on random matrices.
