A gradient-like variational Bayesian approach for joint image super-resolution and source separation, application to astrophysical map-making - CentraleSupélec Access content directly
Conference Papers Year : 2013

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

H Ayasso
GIPSA - Communication Information and Complex Systems
Thomas Rodet
Laboratoire des signaux et systèmes
Alain Abergel
  • Function : Author
Institut d'astrophysique spatiale
Karin Dassas
  • Function : Author
Institut d'astrophysique spatiale

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Signal and Image Processing Signal and Image processing

Thomas Rodet  : Connect in order to contact the contributor

https://centralesupelec.hal.science/hal-00832883

Submitted on : Tuesday, June 11, 2013-3:11:46 PM

Last modification on : Tuesday, May 14, 2024-1:58:11 PM

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hal-00832883 , version 1 (11-06-2013)

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H 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. ICASSP 2013 - 38th IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, Canada. pp.5830-5834, ⟨10.1109/icassp.2013.6638782⟩. ⟨hal-00832883⟩

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