Publications de Matthieu Martin Jean-André Simeoni

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

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

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

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

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, Eric Bezzam, Matthieu Martin Jean-André Simeoni

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

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

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

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

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

Adrian Thibault Etienne Jarret, Matthieu Martin Jean-André Simeoni

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, Matthieu Martin Jean-André Simeoni, Julien René Pierre Fageot, 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