Publications related to Sparse inverse problems for Fourier imaging

Sparse Dictionaries for Continuous-Domain Inverse Problems

Michaël Unser, Shayan Aziznejad, Thomas Jean Debarre

We study 1D continuous-domain inverse problems for multicomponent signals. The prior assumption on these signals is that each component is sparse in a different dictionary specified by a regularization operators. We introduce a hybrid regularization functi ...

2019

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

Kyong Hwan Jin

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

Looking beyond Pixels

Hanjie Pan

Sparse recovery is a powerful tool that plays a central role in many applications, including source estimation in radio astronomy, direction of arrival estimation in acoustics or radar, super-resolution microscopy, and X-ray crystallography. Conventional a ...

EPFL2018

Super Resolution Phase Retrieval for Sparse Signals

Martin Vetterli, Juri Ranieri, Gilles Baechler, Amina Chebira, Miranda Krekovic

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

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

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

Phaseless Diffraction Tomography With Regularized Beam Propagation

Michaël Unser, Demetri Psaltis, Thanh-An Michel Pham, Emmanuel Emilien Louis Soubies, Ferréol Arnaud Marie Soulez, Alexandre Goy, Joowon Lim

In recent years, researchers have obtained impressive reconstructions of the refractive index (RI) of biological objects through the combined use of advanced physics (nonlinear forward model) and regularization. Here, we propose an adaptation of these tech ...

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

A Fourier dimensionality reduction model for big data interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Oxford Univ Press2017

Sparse inverse problems for Fourier imaging

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Sparse Dictionaries for Continuous-Domain Inverse Problems

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

Looking beyond Pixels

Super Resolution Phase Retrieval for Sparse Signals

Compressive Multiplexing of Ultrasound Signals

Compressive Multiplexing of Ultrasound Signals

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

Phaseless Diffraction Tomography With Regularized Beam Propagation

An Adaptive Sublinear-Time Block Sparse Fourier Transform

A Fourier dimensionality reduction model for big data interferometric imaging

Super Resolution Phase Retrieval for Sparse Signals

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

Sparse Dictionaries for Continuous-Domain Inverse Problems

Looking beyond Pixels

A Fourier dimensionality reduction model for big data interferometric imaging

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Compressive Multiplexing of Ultrasound Signals

Compressive Multiplexing of Ultrasound Signals

Phaseless Diffraction Tomography With Regularized Beam Propagation

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