Publications related to Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on t ...

2018

Learning of robust spectral graph dictionaries for distributed processing

Pascal Frossard

We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We ...

SPRINGEROPEN2018

Compressive Multiplexing of Ultrasound Signals

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Marcel Arditi, Adrien Georges Jean Besson

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been deve ...

IEEE2018

Compressive Multiplexing of Ultrasound Signals

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Marcel Arditi, Adrien Besson

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been deve ...

IEEE2018

Adaptive Path Interpolation for Sparse Systems: Application to a Simple Censored Block Model

Nicolas Macris, Jean François Emmanuel Barbier, Chun Lam Chan

A new adaptive path interpolation method has been recently developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gene ...

IEEE2018

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Jean-Philippe Thiran, Martin Vetterli, Dimitris Perdios, Eric Bezzam, Adrien Georges Jean Besson, Hanjie Pan

In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rat ...

IEEE2018

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Jean-Philippe Thiran, Martin Vetterli, Dimitris Perdios, Eric Bezzam, Adrien Besson, Hanjie Pan

In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rat ...

IEEE2018

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Martin Vetterli, Thierry Blu, Hanjie Pan

It is a classic problem to estimate continuous-time sparse signals, like point sources in a direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest occurrence is the estimation of sinusoids in time series using Prony's method. ...

Institute of Electrical and Electronics Engineers2017

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Volkan Cevher, Mikhail Kapralov, Jonathan Mark Scarlett, Amir Zandieh

The problem of approximately computing the

k

dominant Fourier coefficients of a vector

X

quickly, and using few samples in time domain, is known as the Sparse Fourier Transform (sparse FFT) problem. A long line of work on the sparse FFT has resulted in ...

2017

Sparse inverse problems for Fourier imaging

Anna Auria Rasclosa

Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain. The recent theory of Compressed Sensing exploits this property offering a powerful frame ...

EPFL2017

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

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Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Learning of robust spectral graph dictionaries for distributed processing

Compressive Multiplexing of Ultrasound Signals

Compressive Multiplexing of Ultrasound Signals

Adaptive Path Interpolation for Sparse Systems: Application to a Simple Censored Block Model

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Sparse inverse problems for Fourier imaging

Compressive Multiplexing of Ultrasound Signals

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Compressive Multiplexing of Ultrasound Signals

Adaptive Path Interpolation for Sparse Systems: Application to a Simple Censored Block Model

Learning of robust spectral graph dictionaries for distributed processing

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Sparse inverse problems for Fourier imaging

An Adaptive Sublinear-Time Block Sparse Fourier Transform