Publications related to Compressed sensing

Wavelet-Regularized Reconstruction For Rapid MRI

Michaël Unser, Dimitri Nestor Alice Van De Ville, Cédric René Jean Vonesch, Matthieu Guerquin-Kern, Jean-Charles Baritaux

We propose a reconstruction scheme adapted to MRI that takes advantage of a sparsity constraint in the wavelet domain. We show that, artifacts are significantly reduced compared to conventional reconstruction methods. Our approach is also competitive with ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Model-Based Compressive Sensing for Signal Ensembles

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M < N inner products with random vectors and then recover the signal via a sparsity-seeki ...

2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...

2009

Recovery of clustered sparse signals from compressive measurements

Volkan Cevher

We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...

2009

Compressed sensing for radio interferometry: spread spectrum imaging techniques

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

We consider the problem of reconstruction of astrophysical signals probed by radio interferometers with baselines bearing a non-negligible component in the pointing direction. The visibilities measured essentially identify with a noisy and incomplete Fouri ...

2009

CMOS Compressed Imaging by Random Convolution

Pierre Vandergheynst, Alexandre Schmid, Yusuf Leblebici, Laurent Jacques, Vahid Majidzadeh Bafar

We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current is ...

2009

Bit Rate Allocation for Disparity Estimation from Compressed Images

Pascal Frossard, Vijayaraghavan Thirumalai

This paper presents a novel rate allocation scheme to compute the 3D structure of the scene from compressed stereo images, captured by the distributed vision sensor networks. The images captured at different view points are encoded independently with a bal ...

2009

Bit Precision Analysis for Compressed Sensing

Mohammad Amin Shokrollahi, Mahdi Cheraghchi Bashi Astaneh

This paper studies the stability of some reconstruction algorithms for compressed sensing in terms of the bit precision. Considering the fact that practical digital systems deal with discretized signals, we motivate the importance of the total number of ac ...

2009

CMOS Compressed Imaging by Random Convolutions

Pierre Vandergheynst, Alexandre Schmid, Yusuf Leblebici, Laurent Jacques, Vahid Majidzadeh Bafar

We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current is ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Noise-Resilient Group Testing: Limitations and Constructions

Mahdi Cheraghchi Bashi Astaneh

We study combinatorial group testing schemes for learning

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-sparse boolean vectors using highly unreliable disjunctive measurements. We consider an adversarial noise model that only limits the number of false observations, and show that any noise-resilie ...

Springer2009

Compressed sensing

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Wavelet-Regularized Reconstruction For Rapid MRI

Model-Based Compressive Sensing for Signal Ensembles

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Recovery of clustered sparse signals from compressive measurements

Compressed sensing for radio interferometry: spread spectrum imaging techniques

CMOS Compressed Imaging by Random Convolution

Bit Rate Allocation for Disparity Estimation from Compressed Images

Bit Precision Analysis for Compressed Sensing

CMOS Compressed Imaging by Random Convolutions

Noise-Resilient Group Testing: Limitations and Constructions

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

CMOS Compressed Imaging by Random Convolutions

Recovery of clustered sparse signals from compressive measurements

Compressed sensing for radio interferometry: spread spectrum imaging techniques

Model-Based Compressive Sensing for Signal Ensembles

Noise-Resilient Group Testing: Limitations and Constructions

CMOS Compressed Imaging by Random Convolution

Bit Rate Allocation for Disparity Estimation from Compressed Images

Wavelet-Regularized Reconstruction For Rapid MRI

Bit Precision Analysis for Compressed Sensing