Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging

European Signal Processing Conference

Volumn 2002-March, Issue , 2002, Pages

  Cardoso, Jean Francois ^a   Snoussi, Hichem ^b   Delabrouille, Jacques ^c  

^a CNRS   (France)

^b UNIV PARIS SUD   (France)

^c Paris Sciences et Lettres   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; BLIND SOURCE SEPARATION; COSMOLOGY;

ASTRONOMICAL IMAGING; BLIND SEPARATION; COSMIC MICROWAVE BACKGROUNDS; INDEPENDENT COMPONENTS; LOW DIMENSIONAL; SPECTRAL DOMAINS; STATIONARY SOURCES; SUFFICIENT STATISTICS;

SIGNAL PROCESSING;

EID: 84960902656     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. C. Baccigalupi et al. Neural networks and separation of cosmic microwave background and astrophysical signals in sky maps. MNRAS, 318:769-780, Nov. 2000.
2. E. Moulines et al., Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. In Proc. ICASSP'97, vol. 5, pp. 3617-20, 1997.
3. D.-T. Pham. Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion. In Proc. EUSIPCO 2000, pp. 621-624, 2000.
4. H. Snoussi et al., Bayesian blind component separation for cosmic microwave background observations. In Proc. MAXENT 2001, 2001. astro-ph/0109123.
5. J.-F. Cardoso, Looking for components in the Universe's oldest data set. In Proc. EUSIPCO 2002 (these proceedings).