Compressed sensing is a recent technique by which signals can be measured at a rate proportional to their information content, combining the important task of compression directly ...
Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner
When sampling signals below the Nyquist rate, efficient and accurate reconstruction is nevertheless possible, whenever the sampling system is well behaved and the signal is well ...
This paper studies the feasibility and investigates various choices in the application of compressive sensing (CS) to object-based surveillance video coding. The residual object e...
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...