We present two simple methods for recovering sparse signals from a series of noisy
observations. The theory of compressed sensing (CS) requires solving a convex constrained …

Recovery of sparse signals from compressed measurements constitutes an ℓ 0 norm
minimization problem, which is unpractical to solve. A number of sparse recovery …

The fact that fewer measurements are needed by log-sum minimization for sparse signal
recovery than the ℓ 1-minimization has been observed by extensive experiments …

This short note studies a variation of the compressed sensing paradigm introduced recently
by Vaswani et al., ie, the recovery of sparse signals from a certain number of linear …

Compressed sensing aims at reconstructing sparse signals from significantly reduced
number of samples, and a popular reconstruction approach is 1-norm minimization. In this …

In this paper, based on a successively accuracy-increasing approximation of the ℓ 0 norm,
we propose a new algorithm for recovery of sparse vectors from underdetermined …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

We discuss two new methods of recovery of sparse signals from noisy observation based on
ℓ 1-minimization. While they are closely related to the well-known techniques such as Lasso …

The fundamental principle underlying compressed sensing is that a signal, which is sparse
under some basis representation, can be recovered from a small number of linear …

In this correspondence, we introduce a sparse approximation property of order s for a
measurement matrix A:|| xs|| 2≤ D|| Ax|| 2+ β (σ s (x))/√ s for all x, where xs is the best s …