Publications de Matthieu Martin Jean-André Simeoni

PolyCLEAN: Atomic optimization for super-resolution imaging and uncertainty estimation in radio interferometry

Sepand Kashani, Julien René Pierre Fageot, Matthieu Martin Jean-André Simeoni, Joan Rue Queralt, Adrian Thibault Etienne Jarret

Context. Imaging in radio interferometry requires solving an ill-posed noisy inverse problem, for which the most adopted algorithm is the original CLEAN algorithm and its variants. Alternative explicit optimization methods have gained increasing attention, ...

EDP SCIENCES S A2025

BIPP: An efficient HPC implementation of the Bluebild algorithm for radio astronomy

Sepand Kashani, Michele Bianco, Matthieu Martin Jean-André Simeoni, Jean-Paul Richard Kneib, Emma Elizabeth Tolley

The Bluebild algorithm is a new technique for image synthesis in radio astronomy which decomposes the sky into distinct energy levels using functional principal component analysis. These levels can be linearly combined to construct a least-squares estimate ...

2025

DigiCam: Single-Shot Lensless Sensing with a Low-Cost Programmable Mask

Martin Vetterli, Matthieu Martin Jean-André Simeoni, Eric Bezzam

We propose an LCD-based lensless camera for compressive and programmable sensing. The system is low-cost (less than 100 USD) and reproducible. We model the proposed system, and demonstrate experimental results for imaging and classification. ...

Optical Society of America2023

LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi

Martin Vetterli, Sepand Kashani, Matthieu Martin Jean-André Simeoni, Eric Bezzam

This toolkit has everything you need to perform imaging with a lensless camera. We make use of a low-cost implementation of DiffuserCam, namely replacing the lens with a piece of tape and using the Raspberry Pi HQ camera sensor. Similar principles and meth ...

Zenodo2023

pyFFS: A Python Library for Fast Fourier Series Computation and Interpolation with GPU Acceleration

Martin Vetterli, Paul Hurley, Sepand Kashani, Matthieu Martin Jean-André Simeoni, Eric Bezzam

Fourier transforms are an often necessary component in many computational tasks, and can be computed efficiently through the fast Fourier transform (FFT) algorithm. However, many applications involve an underlying continuous signal, and a more natural choi ...

SIAM2022

Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions

Julien René Pierre Fageot, Matthieu Martin Jean-André Simeoni, Adrian Thibault Etienne Jarret

Nous nous intéressons à la reconstruction parcimonieuse d’images à l’aide du problème d’optimisation régularisé LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets à reconstruire limitent, voire empêchent, l’utilisation des ...

2022

A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO

Matthieu Martin Jean-André Simeoni, Adrian Thibault Etienne Jarret

We propose a fast and scalable Polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. The latter improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. ...

IEEE Institute of Electrical and Electronics Engineers2022

Functional estimation of anisotropic covariance and autocovariance operators on the sphere

Victor Panaretos, Julien René Pierre Fageot, Matthieu Martin Jean-André Simeoni, Alessia Caponera

We propose nonparametric estimators for the second-order central moments of possibly anisotropic spherical random fields, within a functional data analysis context. We consider a measurement framework where each random field among an identically distribute ...

2022

Impact Environnemental du Numérique à l’EPFL

Matthieu Martin Jean-André Simeoni, Marlène Hildebrand-Ehrhardt

L’EPFL, institution académique d’envergure internationale, s’est engagée à respecter les Accords de Paris en matière de climat, ratifiés par la Suisse le 6 octobre 2017, et se prépare aujourd’hui à employer son savoir- faire et ses compétences pour opérer ...

2021

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

TV-based reconstruction of periodic functions

Julien René Pierre Fageot, Matthieu Martin Jean-André Simeoni

We introduce a general framework for the reconstruction of periodic multivariate functions from finitely many and possibly noisy linear measurements. The reconstruction task is formulated as a penalized convex optimization problem, taking the form of a sum ...

IOP PUBLISHING LTD2020

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Matthieu Martin Jean-André Simeoni

Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...

EPFL2020

CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI

Martin Vetterli, Paul Hurley, Adrien Georges Jean Besson, Matthieu Martin Jean-André Simeoni

Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for han ...

2020

Functional penalised basis pursuit on spheres

Matthieu Martin Jean-André Simeoni

In this work, we propose a unified theoretical and practical spherical approximation framework for functional inverse problems on the hypersphere. More specifically, we consider recovering spherical fields directly in the continuous domain using functional ...

2020

DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

Martin Vetterli, Paul Hurley, Sepand Kashani, Matthieu Martin Jean-André Simeoni

We propose a recurrent neural-network for real-time reconstruction of acoustic camera spherical maps. The network, dubbed DeepWave, is both physically and algorithmically motivated: its recurrent architecture mimics iterative solvers from convex optimisati ...

2019

A Physical Model of Non-stationary Blur in Ultrasound Imaging

Paul Hurley, Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Matthieu Martin Jean-André Simeoni, Marcel Arditi

Conventional ultrasound (US) imaging relies on delay-and-sum (DAS) beamforming which retrieves a radio- frequency (RF) image, a blurred estimate of the tissue reflectivity function (TRF). Despite the non-stationarity of the blur induced by propagation effe ...

2019

Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

Paul Hurley, Matthieu Martin Jean-André Simeoni

The starting point for deconvolution methods in radioastronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the in ...

IEEE2019

Sparse Spline Approximation on the Hypersphere by Generalised Total Variation Basis Pursuit

Matthieu Martin Jean-André Simeoni

Many scientific inquiries in natural sciences involve approximating a spherical field –namely a scalar quantity defined over a continuum of directions– from generalised samples of the latter. Typically, a convex optimisation problem is formulated in terms ...

2019

A Functional Framework For Ultrasound Imaging

Paul Hurley, Matthieu Martin Jean-André Simeoni

Delay-And-Sum (DAS), the state-of-art in ultrasound imaging, is known to be sub-optimal, resulting in low resolution and contrast. Most proposed improvements involve ad-hoc re-weighting, or hit computational bottlenecks given real-time requirements. This p ...

IEEE2018

Towards More Accurate Radio Telescope Images

Paul Hurley, Matthieu Martin Jean-André Simeoni, Nezihe Merve Gürel

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. We propose instead a method to remove the noise explicitly before ima ...

IEEE2018