Abstract This paper focuses on the sufficient condition of block sparse recovery with the l 2 / l 1 $l_{2}/l_{1}$ -minimization. We show that if the measurement matrix satisfies the block restricted isometry property with δ 2 s | I < 0.6246 $\delta_{2s|\mathcal{I}}< 0.6246$ , then every block s-sparse signal can be exactly recovered via the l 2 / l 1 $l_{2}/l_{1}$ -minimization approach in the noiseless case and is stably recovered in the noisy measurement case. The result improves the bound on the block restricted isometry constant δ 2 s | I $\delta_{2s|\mathcal {I}}$ of Lin and Li (Acta Math. Sin. Engl. Ser. 29(7):1401-1412, 2013)
We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform...
AbstractIn the theory of compressed sensing, restricted isometry analysis has become a standard tool...
The problem of recovering a low-rank matrix consistent with noisy linear measurements is a fundament...
This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an...
AbstractRestricted isometry constants play an important role in compressed sensing. In the literatur...
This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an...
This note discusses the recovery of signals from undersampled data in the situation that such signal...
In this thesis we give an overview of the notion of compressed sensing together with some special ty...
Received:28/07/2013 Accepted:28/10/2014 Compressed sensing seeks to recover an unknown sparse signal...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
Abstract. Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fe...
Compressed sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N...
It has become an established fact that the constrained `1 minimization is capable of recovering the ...
This paper establishes new restricted isometry conditions for compressed sensing and affine rank min...
International audienceWe propose novel necessary and sufficient conditions for a sensing matrix to b...
We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform...
AbstractIn the theory of compressed sensing, restricted isometry analysis has become a standard tool...
The problem of recovering a low-rank matrix consistent with noisy linear measurements is a fundament...
This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an...
AbstractRestricted isometry constants play an important role in compressed sensing. In the literatur...
This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an...
This note discusses the recovery of signals from undersampled data in the situation that such signal...
In this thesis we give an overview of the notion of compressed sensing together with some special ty...
Received:28/07/2013 Accepted:28/10/2014 Compressed sensing seeks to recover an unknown sparse signal...
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processi...
Abstract. Compressed Sensing (CS) seeks to recover an unknown vector with N entries by making far fe...
Compressed sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N...
It has become an established fact that the constrained `1 minimization is capable of recovering the ...
This paper establishes new restricted isometry conditions for compressed sensing and affine rank min...
International audienceWe propose novel necessary and sufficient conditions for a sensing matrix to b...
We consider two theorems from the theory of compressive sensing. Mainly a theorem concerning uniform...
AbstractIn the theory of compressed sensing, restricted isometry analysis has become a standard tool...
The problem of recovering a low-rank matrix consistent with noisy linear measurements is a fundament...