A gradient-like variational Bayesian approach for joint image super-resolution and source separation, application to astrophysical map-making

Hacheme Ayasso 1 Thomas Rodet 2, 3 Alain Abergel 4 Karin Dassas 4
1 GIPSA-CICS - CICS
GIPSA-DIS - Département Images et Signal
2 Division Signaux - L2S
L2S - Laboratoire des signaux et systèmes : 1289
Complete list of metadatas

https://hal-supelec.archives-ouvertes.fr/hal-00832883
Contributor : Thomas Rodet <>
Submitted on : Tuesday, June 11, 2013 - 3:11:46 PM
Last modification on : Friday, April 5, 2019 - 8:11:43 PM

Identifiers

  • HAL Id : hal-00832883, version 1

Citation

Hacheme Ayasso, Thomas Rodet, Alain Abergel, Karin Dassas. A gradient-like variational Bayesian approach for joint image super-resolution and source separation, application to astrophysical map-making. 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), May 2013, vancouver, Canada. pp.5830-5834. ⟨hal-00832883⟩

Share

Metrics

Record views

401