The paper discusses the relationship between the null space property (NSP) and the lq‐
minimization in compressed sensing. Several versions of the null space property, that is, the …

The null space property (NSP) and the restricted isometry property (RIP) are two properties
which have received considerable attention in the compressed sensing literature. As the …

Biometric systems allow recognition and verification of an individual through his or her
physiological or behavioral characteristics. It is a growing field of research due to the …

Solving compressed sensing problems relies on the properties of sparse signals. It is
commonly assumed that the sparsity s needs to be less than one half of the spark of the …

We consider the problem of compressed sensing with a coherent tight frame and design an
iteratively reweighted least squares algorithm to solve it. To analyze the problem, we …

Scaling frame vectors is a simple and noninvasive way to construct tight frames. However,
not all frames can be modified to tight frames in this fashion, so in this case we explore the …

We introduce a projective Riesz s-kernel for the unit sphere S^ d-1 S d-1 and investigate
properties of N-point energy minimizing configurations for such a kernel. We show that these …

Abstract We study\(l_0\)-synthesis/analysis methods and the thresholding-based algorithms
for the dictionary-sparse recovery from a few linear measurements perturbed with Gaussian …

This work investigates the problem of signal recovery from undersampled noisy sub-
Gaussian measurements under the assumption of a synthesis-based sparsity model …

Signal models formed as linear combinations of few atoms from an over-complete dictionary
or few frame vectors from a redundant frame have become central to many applications in …