Compressive sensing (CS) states that a sparse signal can be recovered from a small
number of linear measurements, and that this recovery can be performed efficiently in …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

Compressive sensing is a method for recording ak-sparse signal x∈ ℝ n with (possibly
noisy) linear measurements of the form y= Ax, where A∈ ℝ m× n describes the …

The goal of sparse recovery is to recover ak-sparse signal x∊ ℝ n from (possibly noisy)
linear measurements of the form y= Ax, where A∊ ℝ m× n describes the measurement …

Compressive sensing (CS) has recently emerged as a powerful framework for acquiring
sparse signals. The bulk of the CS literature has focused on the case where the acquired …

Most of the recent compressive sensing (CS) literature has focused on sparse signal
recovery based on compressive measurements. However, exact signal recovery may not be …

In compressive sensing, prior information about the sparse representation's support reduces
the theoretical minimum number of measurements that allows perfect reconstruction. This …

The recently introduced compressive sensing (CS) framework enables digital signal
acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed …

The recently introduced theory of compressive sensing enables the recovery of sparse or
compressible signals from a small set of nonadaptive, linear measurements. If properly …

Compressive sensing (CS) is an emerging field based on the revelation that a small
collection of linear projections of a sparse signal contains enough information for stable, sub …