Publications associées à Sampling Bounds for Sparse Support Recovery in the Presence of Noise

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Differences between observation and sampling error in sparse signal reconstruction

Michael Christoph Gastpar

The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Martin Vetterli, Ali Hormati

Recent results in compressed sensing or compressive sampling suggest that a relatively small set of measurements taken as the inner product with universal random measurement vectors can well represent a source that is sparse in some fixed basis. By adaptin ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2007

Distributed sensing of noisy signals by thresholding of redundant expansions

Pascal Frossard, Pierre Vandergheynst, Karin Schnass

This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximan ...

2006

Dynamic Texture Analysis and Synthesis using Tensor Decomposition

Sabine Süsstrunk, Luciano Sbaiz, Roberto Costantini

Dynamic textures are sequences of images showing temporal regularity, such as smoke, flames, flowing water, or moving grass. Despite being a multidimensional signal, existing models reshape the dynamic texture into a 2D signal for analysis. In this article ...

2006

Sparse decomposition over multi-component redundant dictionaries

Pierre Vandergheynst, Lorenzo Granai

In many applications - such as compression, de-noising and source separation - a good and efficient signal representation is characterized by sparsity. This means that many coefficients are close to zero, while only few ones have a non-negligible amplitude ...

IEEE2004

Sampling signals with finite rate of innovation: the noisy case

Martin Vetterli, Andrea Ridolfi, Irena Maravic

Recently a sampling theorem for a certain class of signals with finite rate of innovation (which includes for example stream of Diracs) has been developed. In essence, such non band-limited signals can be sampled at or above the rate of innovation. In the ...

2002

Sampling signals with finite rate of innovation

Martin Vetterli, Pina Marziliano, Thierry Blu

Consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform ...

Institute of Electrical and Electronics Engineers2002

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

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Differences between observation and sampling error in sparse signal reconstruction

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Distributed sensing of noisy signals by thresholding of redundant expansions

Dynamic Texture Analysis and Synthesis using Tensor Decomposition

Sparse decomposition over multi-component redundant dictionaries

Sampling signals with finite rate of innovation: the noisy case

Sampling signals with finite rate of innovation

Sampling innovations

Annihilating filter-based decoding in the compressed sensing framework - art. no. 670121

Sampling signals with finite rate of innovation

Sampling signals with finite rate of innovation: the noisy case

Sampling innovations

Differences between observation and sampling error in sparse signal reconstruction

Sparse decomposition over multi-component redundant dictionaries

Distributed sensing of noisy signals by thresholding of redundant expansions

Dynamic Texture Analysis and Synthesis using Tensor Decomposition