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We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists of unknown complex gains on each measure. We focus on blind calibration, using measures performed on a few unknown (but spar ...

We investigate a compressive sensing system in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on a few unknown (but sparse) signals. We extend our earlier s ...

Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a r ...

We present a novel method for robust reconstruction of the image of a moving object from incomplete linear measurements. We assume that only few measurements of this object can be acquired at different instants and model the correlation between measurement ...

We introduce a new sparse recovery paradigm, called Normed Pursuits, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that Normed Pur ...

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

We present a method for joint reconstruction of a set of images representing a given scene from few multi-view measurements obtained by compressed sensing. We model the correlation between measurements using global geometric transformations represented by ...

We discuss a novel sampling theorem on the sphere developed by McEwen & Wiaux recently through an association between the sphere and the torus. To represent a band-limited signal exactly, this new sampling theorem requires less than half the number of samp ...

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

We consider the problem of reconstruction of astrophysical signals probed by radio interferometers with baselines bearing a non-negligible component in the pointing direction. The visibilities measured essentially identify with a noisy and incomplete Fouri ...

Respiratory motion is a major challenge in cardiac magnetic resonance imaging (MRI) and contemporary state-of-the-art motion compensation strategies like diaphragmatic navigators still suffer from sub-optimal time efficiency. In response, k-space- based on ...

A sampling theorem on the sphere has been developed recently, requiring half as many samples as alternative equiangular sampling theorems on the sphere. A reduction by a factor of two in the number of samples required to represent a band-limited signal on ...

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

We propose a method for signal reconstruction in semi-parametric dictionaries. The proposed algorithm estimates both the signal decomposition and the intrinsic parameters of the dictionary during the reconstruction process. Theoretical results about the co ...

We consider images probed through incomplete and noisy Fourier coverages, both in the context of radio interferometry (RI) and of magnetic resonance imaging (MRI). We show that the quality of signal reconstruction can be signiﬁcantly enhanced by the introd ...

Les méthodes de localisation de sources acoustiques à l'aide de réseaux de microphones se limitent généralement à l'étude de sources à bande étroite situées en champ lointain. Le travail présenté a pour objectif de réaliser conjointement la localisation et ...

The main challenge for cardiac MRI is motion. To account for respiratory motion, k-space-based self- navigation approaches have recently been introduced [1]. We took self-navigation to the next level by exploiting an image-based beat-to-beat respiratory mo ...

We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a ...

We propose and assess the performance of new imaging techniques for radio interferometry that rely on the versatility of the compressed sensing framework to account for prior information on the signals. The present manuscript represents a summary of recent ...

Gilles Puy

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Blind Sensor Calibration in Sparse Recovery

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

Joint reconstruction of misaligned images from incomplete measurements for cardiac MRI

Hard Thresholding with Norm Constraints

Compressive Spectral Clustering

Joint image registration and reconstruction from compressed multi-view measurements

Sampling theorems and compressive sensing on the sphere

Random sampling of bandlimited signals on graphs

Efficient compressive sampling strategies and novel reconstruction methods with applications in MRI

Compressed sensing for radio interferometry: spread spectrum imaging techniques

About the performance of multi-dimensional radial self-navigation incorporating compressed sensing for free-breathing coronary MRI

Implications for compressed sensing of a new sampling theorem on the sphere

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Robust joint reconstruction of misaligned images using semi-parametric dictionaries

Spread spectrum for interferometric and magnetic resonance imaging

Localisation et identification spectrale conjointe de sources large bande par parcimonie groupée

Multi-dimensional radial self-navigation with non-linear reconstruction for free-breathing coronary MRI

Spread spectrum for imaging techniques in radio interferometry

Compressed sensing for radio interferometry: prior-enhanced Basis Pursuit imaging techniques