29 citations · 132 across the 19 of their papers we have counts for
22 papers
Neural posterior estimation of Galactic Binary signals for the LISA mission
Tanguy Delmond, Natalia Korsakova, Thomas Oberlin +3
ESA's LISA mission will open a new window onto the gravitational-wave sky by detecting signals from a wide variety of sources in the millihertz frequency band. Among these, galacti…
A recurrent CNN for online object detection on raw radar frames
Colin Decourt, Rufin VanRullen, Didier Salle +1
Automotive radar sensors provide valuable information for advanced driving assistance systems (ADAS). Radars can reliably estimate the distance to an object and the relative veloci…
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
Ondřej Mokrý, Paul Magron, Thomas Oberlin +1
Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propo…
Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
This paper considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handl…
Regularization via deep generative models: an analysis point of view
Thomas Oberlin, Mathieu Verm
This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end mo…
Phase retrieval with Bregman divergences: Application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval aims to recover a signal from magnitude or power spectra measurements. It is often addressed by considering a minimization problem involving a quadratic cost functi…
Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images
Yanna Cruz Cavalcanti, Thomas Oberlin, Vinicius Ferraris +3
When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Fact…
Phase recovery with Bregman divergences for audio source separation
Paul Magron, Pierre-Hugo Vial, Thomas Oberlin +1
Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algori…
Phase retrieval with Bregman divergences and application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a sh…
Ordinal Non-negative Matrix Factorization for Recommendation
Olivier Gouvert, Thomas Oberlin, Cédric Févotte
We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the ca…
Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling
Etienne Monier, Thomas Oberlin, Nathalie Brun +3
This paper discusses the reconstruction of partially sampled spectrum-images to accelerate the acquisition in scanning transmission electron microscopy (STEM). The problem of image…
Simulated JWST datasets for multispectral and hyperspectral image fusion
Claire Guilloteau, Thomas Oberlin, Olivier Berné +1
This paper aims at providing a comprehensive framework to generate an astrophysical scene and to simulate realistic hyperspectral and multispectral data acquired by two JWST instru…
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
Claire Guilloteau, Thomas Oberlin, Olivier Berné +1
Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acqui…
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Olivier Gouvert, Thomas Oberlin, Cédric Févotte
Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit…
Factor analysis of dynamic PET images: beyond Gaussian noise
Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon +4
Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data.…
Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images
Vinicius Ferraris, Nicolas Dobigeon, Yanna Cavalcanti +2
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations,…
Reconstruction of partially sampled multi-band images - Application to STEM-EELS imaging
Étienne Monier, Thomas Oberlin, Nathalie Brun +3
Electron microscopy has shown to be a very powerful tool to map the chemical nature of samples at various scales down to atomic resolution. However, many samples can not be analyze…
Negative Binomial Matrix Factorization for Recommender Systems
Olivier Gouvert, Thomas Oberlin, Cédric Févotte
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an exte…
Unmixing dynamic PET images with variable specific binding kinetics
Yanna Cruz Cavalcanti, Thomas Oberlin, Nicolas Dobigeon +3
To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal compon…
EEG reconstruction and skull conductivity estimation using a Bayesian model promoting structured sparsity
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
M/EEG source localization is an open research issue. To solve it, it is important to have good knowledge of several physical parameters to build a reliable head operator. Amongst t…
Bayesian Structured Sparsity Priors for EEG Source Localization Technical Report
Facundo Costa, Hadj Batatia, Thomas Oberlin +1
This report introduces a new hierarchical Bayesian model for the EEG source localization problem. This model promotes structured sparsity to search for focal brain activity. This s…
The Monogenic Synchrosqueezed Wavelet Transform: A tool for the Decomposition/Demodulation of AM-FM images
Marianne Clausel, Thomas Oberlin, Valérie Perrier
The synchrosqueezing method aims at decomposing 1D functions as superpositions of a small number of "Intrinsic Modes", supposed to be well separated both in time and frequency. Bas…