Multichannel deconvolution and source detection using sparse representations : application to Fermi project
type: Dataset
description: This thesis presents new methods for spherical Poisson data analysis for the Fermi mission. Fermi main scientiﬁcal objectives, the study of diﬀuse galactic background et the building of the source catalog, are complicated by the weakness of photon ﬂux and the point spread function of the instrument. This thesis proposes a new multi-scale representation for Poisson data on the sphere, the Multi-Scale Variance Stabilizing Transform on the Sphere (MS-VSTS), consisting in the combination of a spherical multi-scale transform (wavelets, curvelets) with a variance stabilizing transform (VST). This method is applied to mono- and multichannel Poisson noise removal, missing data interpolation, background extraction and multichannel deconvolution. Finally, this thesis deals with the problem of component separation using sparse representations (template ﬁtting ).
identifier: NNT: 2011PA112313
issued: 2026-05-28T16:00:04.842426
language: fr
modified: 2026-05-28T16:00:04.842432
contact point: Organization
  fn: CCSD
keyword: debruitage
keyword: deconvolution
keyword: denoising
keyword: donnees-multicanales
keyword: image-processing
keyword: infoeu-reposemanticsdoctoralthesis
keyword: multichannel-data
keyword: multiscale-representations
keyword: ondelettes
keyword: physcondcm-genphysics-physicscondensed-matter-cond-matother-cond-matother
keyword: representations-multiechelle
keyword: representations-parcimonieuses
keyword: sparse-representations
keyword: theses
keyword: traitement-dimages
keyword: wavelets
landing page: https://theses.hal.science/tel-00670302

Multichannel deconvolution and source detection using sparse representations : application to Fermi project
type: Distribution
format: HTML
issued: 2026-05-28T16:00:04.858786
modified: 2026-05-28T16:00:04.786216
access URL: https://theses.hal.science/tel-00670302

test_moissonnage_selune
type: Agent