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

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

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

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

2019

High-fidelity optical diffraction tomography of multiple scattering samples

Demetri Psaltis, Joowon Lim, Elizabeth Elena Antoine, Ahmed Ayoub

We propose an iterative reconstruction scheme for optical diffraction tomography that exploits the split-step nonparaxial (SSNP) method as the forward model in a learning tomography scheme. Compared with the beam propagation method (BPM) previously used in ...

Springer2019

Sampling the Multiple Facets of Light

Gilles Baechler

The theme of this thesis revolves around three important manifestations of light, namely its corpuscular, wave and electromagnetic nature. Our goal is to exploit these principles to analyze, design and build imaging modalities by developing new signal proc ...

EPFL2018

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

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

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

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

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

Sparse Reverberant Audio Source Separation via Reweighted Analysis

Jean-Philippe Thiran, Pierre Vandergheynst, Yves Wiaux, Rafael Eduardo Carrillo Rangel

We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse p ...

Institute of Electrical and Electronics Engineers2013

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

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Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

High-fidelity optical diffraction tomography of multiple scattering samples

Sampling the Multiple Facets of Light

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

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

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Compressed Sensing of Memoryless Sources

Sparse Reverberant Audio Source Separation via Reweighted Analysis

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Compressed Sensing of Memoryless Sources

Sampling the Multiple Facets of Light

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

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

An Adaptive Sublinear-Time Block Sparse Fourier Transform

Sparse Reverberant Audio Source Separation via Reweighted Analysis

High-fidelity optical diffraction tomography of multiple scattering samples

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging