Différences
Ci-dessous, les différences entre deux révisions de la page.
| Les deux révisions précédentes Révision précédente | Prochaine révision Les deux révisions suivantes | ||
|
recherche:projets:circumstellar2021 [2021/04/01 16:00] equemene [Circumstellar environments reconstruction with deep learning] |
recherche:projets:circumstellar2021 [2021/04/01 16:00] equemene [Circumstellar environments reconstruction with deep learning] |
||
|---|---|---|---|
| Ligne 10: | Ligne 10: | ||
| Following recent advances in deep learning for image restoration [2], the objective of this new work is to explore such framework in the context of high-contrast reconstruction for studying cIrcumstellar environments. Using as a starting point the direct model and the algorithmic strategy provided in [1], we will unroll the iterations to fit a deep learning formalism. | Following recent advances in deep learning for image restoration [2], the objective of this new work is to explore such framework in the context of high-contrast reconstruction for studying cIrcumstellar environments. Using as a starting point the direct model and the algorithmic strategy provided in [1], we will unroll the iterations to fit a deep learning formalism. | ||
| - | Référence : | + | Références : |
| * [1] L. Denneulin, M. Langlois, E. Thiebaut, and N. Pustelnik, RHAPSODIE : Reconstruction of High-contrAst Polarized SOurces and Deconvolution for cIrcumstellar Environments, submitted, 2020. | * [1] L. Denneulin, M. Langlois, E. Thiebaut, and N. Pustelnik, RHAPSODIE : Reconstruction of High-contrAst Polarized SOurces and Deconvolution for cIrcumstellar Environments, submitted, 2020. | ||
| * [2] M. Jiu, N. Pustelnik, A deep primal-dual proximal network for image restoration, accepted to IEEE JSTSP, 2021. | * [2] M. Jiu, N. Pustelnik, A deep primal-dual proximal network for image restoration, accepted to IEEE JSTSP, 2021. | ||