Publications related to Super Resolution Phase Retrieval for Sparse Signals

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

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

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

Method for estimating synchrophasors during static and dynamic conditions

Mario Paolone, Asja Derviskadic, Paolo Romano

A method for performing synchrophasor estimation of an input signal, whereby the input signal is a sinusoidal power system voltage or current signal, comprising: a. Periodic sampling of a continuous time-domain waveform, consisting of a power system voltag ...

2018

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

Martin Vetterli, Benjamin Bejar Haro

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

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

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

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Kyong Hwan Jin

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

IEEE2017

FRIDA: FRI-Based DOA Estimation For Arbitrary Array Layouts

Martin Vetterli, Eric Bezzam, Ivan Dokmanic, Robin Scheibler, Hanjie Pan

In this paper we present FRIDA—an algorithm for estimating directions of arrival of multiple wideband sound sources. FRIDA combines multi-band information coherently and achieves state- of-the-art resolution at extremely low signal-to-noise ratios. It work ...

IEEE2017

Super Resolution Phase Retrieval for Sparse Signals

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

Compressive Multiplexing of Ultrasound Signals

Compressive Multiplexing of Ultrasound Signals

Method for estimating synchrophasors during static and dynamic conditions

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

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

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Sparse inverse problems for Fourier imaging

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

FRIDA: FRI-Based DOA Estimation For Arbitrary Array Layouts

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

Method for estimating synchrophasors during static and dynamic conditions

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

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

Compressive Multiplexing of Ultrasound Signals

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Compressive Multiplexing of Ultrasound Signals

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

FRIDA: FRI-Based DOA Estimation For Arbitrary Array Layouts

Sparse inverse problems for Fourier imaging