Inverse scattering from discrete targets with the single-input-multiple-output (SIMO), multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) measurements is analyzed by compressed sensing theory with and without the Born approximation. High frequency analysis of (probabilistic) recoverability by the $L^1$-based minimization/regularization principles is presented. In the absence of noise, it is shown that the $L^1$-based solution can recover exactly the target of sparsity up to the dimension of the data either with the MIMO measurement for the Born scattering or with the SIMO/MISO measurement for the exact scattering. The stability with respect to noisy dat...
International audienceThe Compressive Sensing (CS) paradigm recently emerged as an effective strateg...
International audienceThis work is devoted to the presentation of the recent advances of Compressive...
We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. T...
Abstract. Inverse scattering from discrete targets with the single-input-multiple-output (SIMO), mul...
Abstract. Inverse scattering from discrete targets with the single-input-multiple-output (SIMO), mul...
Abstract. Inverse scattering methods capable of compressive imaging are proposed and ana-lyzed. The ...
Inverse scattering refers the retrieval of the unknown constitutive parameters from measured scatter...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
It is well-known that in a canonical inverse scattering problem there is only a limited amount of in...
The linear inverse source and scattering problems are studied from the perspective of compressed se...
International audienceThe application of the Compressive Sensing (CS) paradigm to the solution of th...
Abstract. This paper proposes a general framework for compressed sensing of constrained joint sparsi...
This paper proposes a general framework for compressed sensing of constrained joint sparsit...
International audienceThis paper proposes a general framework for compressed sensing of constrained ...
An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-spars...
International audienceThe Compressive Sensing (CS) paradigm recently emerged as an effective strateg...
International audienceThis work is devoted to the presentation of the recent advances of Compressive...
We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. T...
Abstract. Inverse scattering from discrete targets with the single-input-multiple-output (SIMO), mul...
Abstract. Inverse scattering from discrete targets with the single-input-multiple-output (SIMO), mul...
Abstract. Inverse scattering methods capable of compressive imaging are proposed and ana-lyzed. The ...
Inverse scattering refers the retrieval of the unknown constitutive parameters from measured scatter...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
It is well-known that in a canonical inverse scattering problem there is only a limited amount of in...
The linear inverse source and scattering problems are studied from the perspective of compressed se...
International audienceThe application of the Compressive Sensing (CS) paradigm to the solution of th...
Abstract. This paper proposes a general framework for compressed sensing of constrained joint sparsi...
This paper proposes a general framework for compressed sensing of constrained joint sparsit...
International audienceThis paper proposes a general framework for compressed sensing of constrained ...
An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-spars...
International audienceThe Compressive Sensing (CS) paradigm recently emerged as an effective strateg...
International audienceThis work is devoted to the presentation of the recent advances of Compressive...
We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. T...