Image Reconstruction in Optical Interferometry

This tutorial paper describes the problem of image reconstruction from interferometric data with a particular focus on the specific problems encountered at optical (visible/IR) wavelengths. The challenging issues in image reconstruction from interferometric data are introduced in the general framework of inverse problem approach. This framework is then used to describe existing image reconstruction algorithms in radio interferometry and the new methods specifically developed for optical interferometry.Comment: accepted for publication in IEEE Signal Processing Magazin

Gradient Scan Gibbs Sampler: an efficient algorithm for high-dimensional Gaussian distributions

This paper deals with Gibbs samplers that include high dimensional conditional Gaussian distributions. It proposes an efficient algorithm that avoids the high dimensional Gaussian sampling and relies on a random excursion along a small set of directions. The algorithm is proved to converge, i.e. the drawn samples are asymptotically distributed according to the target distribution. Our main motivation is in inverse problems related to general linear observation models and their solution in a hierarchical Bayesian framework implemented through sampling algorithms. It finds direct applications in semi-blind/unsupervised methods as well as in some non-Gaussian methods. The paper provides an illustration focused on the unsupervised estimation for super-resolution methods.Comment: 18 page

Estimation du paramètre du champ de Ising et fonction de partition

International audienceLe papier propose de nouveaux estimateurs du paramètre du champ de Ising. Ils sont fondés sur une expression explicite et relativement simple de la fonction de partition du champ. Celle-ci est connue de la physique statistique depuis longtemps [Onsager 44], mais, à notre connaissance, elle n'a jamais été exploitée pour des méthodes d'estimation du paramètre. Tirant partie de ce résultat, le papier propose plusieurs estimateurs fondés sur la vraisemblance exacte et concurrents de stratégies existantes fondées sur la pseudo-vraisemblance. Une étude numérique en terme de biais et variance en fonction de la vraie valeur du paramètre montre que les estimateurs proposés offrent des performances largement meilleures que celui fondé sur la pseudo-vraisemblance

Sampling high-dimensional Gaussian distributions for general linear inverse problems

International audienceThis paper is devoted to the problem of sampling Gaussian distributions in high dimension. Solutions exist for two specific structures of inverse covariance: sparse and circulant. The proposed algorithm is valid in a more general case especially as it emerges in linear inverse problems as well as in some hierarchical or latent Gaussian models. It relies on a perturbation-optimization principle: adequate stochastic perturbation of a criterion and optimization of the perturbed criterion. It is proved that the criterion optimizer is a sample of the target distribution. The main motivation is in inverse problems related to general (non-convolutive) linear observation models and their solution in a Bayesian framework implemented through sampling algorithms when existing samplers are infeasible. It finds a direct application in myopic,unsupervised inversion methods as well as in some non-Gaussian inversion methods. An illustration focused on hyperparameter estimation for super-resolution method shows the interest and the feasibility of the proposed algorithm

Bayesian hierarchical reconstruction of protein profiles including a digestion model

Introduction : Mass spectrometry approaches are very attractive to detect protein panels in a sensitive and high speed way. MS can be coupled to many proteomic separation techniques. However, controlling technological variability on these analytical chains is a critical point. Adequate information processing is mandatory for data analysis to take into account the complexity of the analysed mixture, to improve the measurement reliability and to make the technology user friendly. Therefore we develop a hierarchical parametric probabilistic model of the LC-MS analytical chain including the technological variability. We introduce a Bayesian reconstruction methodology to recover the protein biomarkers content in a robust way. We will focus on the digestion step since it brings a major contribution to technological variability. Method : In this communication, we introduce a hierarchical model of the LC-MS analytical chain. Such a chain is a cascade of molecular events depicted by a graph structure, each node being associated to a molecular state such as protein, peptide and ion and each branch to a molecular processing such as digestion, ionisation and LC-MS separation. This molecular graph defines a hierarchical mixture model. We extend the Bayesian statistical framework we have introduced previously [1] to this hierarchical description. As an example, we will consider the digestion step. We describe the digestion process on a pair of peptides within the targeted protein as a Bernoulli random process associated with a cleavage probability controlled by the digestion kinetic law.Comment: pr\'esentation orale; 59th American Society for Mass Spectrometry Conference, Dallas : France (2011

Data Inversion for Over-Resolved Spectral Imaging in Astronomy

International audienceWe present an original method for reconstructing a 3-D object having two spatial dimensions and one spectral dimension from data provided by the infrared slit spectrograph on board the Spitzer Space Telescope. During acquisition, the light flux is deformed by a complex process comprising four main elements (the telescope aperture, the slit, the diffraction grating, and optical distortion) before it reaches the 2-D sensor. The originality of this work lies in the physical modeling, in integral form, of this process of data formation in continuous variables. The inversion is also approached with continuous variable in a semi-parametric format decomposing the object into a family of Gaussian functions. The estimate is built in a deterministic regularization framework as the minimizer of a quadratic criterion. These specificities give our method the power to over-resolve. It performance is illustrated using real and simulated data. We also present a study of the resolution showing a 1.5-fold improvement relative to conventional methods

Joint Bayesian Hierarchical Inversion-Classification and Application in Proteomics

nb de pages: 4International audienceIn this paper, we combine inverse problem and classification for LC-MS data in a joint Bayesian context, given a set of biomarkers and the statistical characteristics of the biological classes. The data acquisition is modelled in a hierarchical way, including random decomposition of proteins into peptides and peptides into ions associated to peaks on the LC-MS measurement. A Bayesian global inversion, based on the hierarchical model for the direct problem, enables to take into account the biological and technological variabilities from those random processes and to estimate the parameters efficiently. We describe the statistical theoretical framework including the hierarchical direct model, the prior and posterior distributions and the estimators for the involved parameters. We resort to the MCMC algorithm and give preliminary results on a simulated data set

Inversion de données pour l'imagerie spectrale sur-résolue en astronomie

International audienceNous nous intéressons à l'inversion de données infrarouges issues du spectromètre IRS du satellite SPITZER. Les obstacles rencontrés sont la complexité de l'instrument et un phénomène de sous-échantillonnage. À l'aide d'un modèle instrument fidèle et rapide et la redondance des données nous avons développé une méthode originale d'estimation d'un ciel sur-résolu. Elle repose sur l'inversion des données par minimisation d'un critère quadratique calculé avec un algorithme de descente. Les premiers résultats mettent en évidence un gain significatif en résolution