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Recovery of Compressible Signals in Unions of Subspaces

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Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Pierre Vandergheynst, Alexandre Massoud Alahi, Laurent Jacques

In this paper, we propose to study the problem of localization of a dense set of people with a network of heterogeneous cameras. We propose to recast the problem as a linear inverse problem. The proposed framework is generic to any scene, scalable in the n ...

Springer Verlag2011

Model-based compressive sensing

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K < N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner ...

Institute of Electrical and Electronics Engineers2010

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Sparse Signal Acquisition and Recovery with Graphical Models

Volkan Cevher

A great deal of theoretic and algorithmic research has revolved around sparsity view of signals over the last decade to characterize new, sub-Nyquist sampling limits as well as tractable algorithms for signal recovery from dimensionality reduced measuremen ...

Institute of Electrical and Electronics Engineers2010

Compressive Sensing of Localized Signals: Application to Analog-to-Information Conversion

Juri Ranieri

Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a limited number of measures. When reconstruction is possible, the SNR of the reconstructed signal depends on the energy collected in the acquisition. Hence, i ...

2010

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

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Laurent Jacques

The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...

2009

Dequantizing Compressed Sensing with non-Gaussian Constraints

Laurent Jacques

2009

Sparsity & [and] dictionaries - algorithms & [and] design

Karin Schnass

With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...

EPFL2009

Compressive Sampling of Pulse Trains : Spread the Spectrum !

Pierre Vandergheynst, Rémi Gribonval, Laurent Jacques

In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...

2009