In this paper we define a new coherence index, named the global 2-coherence, of a given dictionary and study its relationship with the traditional mutual coherence and the restricted isometry constant. By exploring this relationship, we obtain more general results on sparse signal reconstruction using greedy algorithms in the compressive sensing (CS) framework. In particular, we obtain an improved bound over the best known results on the restricted isometry constant for successful recovery of sparse signals using orthogonal matching pursuit (OMP). Index Terms Compressive sensing, mutual coherence, global 2-coherence, restricted isometry property, weak orthogonal match-ing pursuit (WOMP), orthogonal matching pursuit (OMP) I
It is known that use of a random measurement (sensing) matrix usually results in good recovery perfo...
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparse...
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruc...
In this paper we define a new coherence index, named 2-coherence, of a given dictionary and study it...
IEEE Compressive Sampling Matching Pursuit(CoSaMP) and Subspace Pursuit(SP) are popular compressive ...
A recent result establishing, under restricted isometry conditions, the success of sparse recovery v...
In this paper, we present a new performance guarantee for the orthogonal matching pursuit (OMP) algo...
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. ...
In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pur...
AbstractThis article presents novel results concerning the recovery of signals from undersampled dat...
International audienceWe propose extended coherence-based conditions for exact sparse support recove...
This article presents novel results concerning the recovery of signals from undersampled data in the...
This article presents novel results concerning the recovery of signals from undersampled data in the...
Abstract. Inspired by significant real-life applications, in particular, sparse phase retrieval and ...
For recovering block-sparse signals with unknown block structures using compressive sensing, a block...
It is known that use of a random measurement (sensing) matrix usually results in good recovery perfo...
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparse...
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruc...
In this paper we define a new coherence index, named 2-coherence, of a given dictionary and study it...
IEEE Compressive Sampling Matching Pursuit(CoSaMP) and Subspace Pursuit(SP) are popular compressive ...
A recent result establishing, under restricted isometry conditions, the success of sparse recovery v...
In this paper, we present a new performance guarantee for the orthogonal matching pursuit (OMP) algo...
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. ...
In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pur...
AbstractThis article presents novel results concerning the recovery of signals from undersampled dat...
International audienceWe propose extended coherence-based conditions for exact sparse support recove...
This article presents novel results concerning the recovery of signals from undersampled data in the...
This article presents novel results concerning the recovery of signals from undersampled data in the...
Abstract. Inspired by significant real-life applications, in particular, sparse phase retrieval and ...
For recovering block-sparse signals with unknown block structures using compressive sensing, a block...
It is known that use of a random measurement (sensing) matrix usually results in good recovery perfo...
In this paper, we consider the problem of compressed sensing where the goal is to recover all sparse...
The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruc...