A variational Bayesian approach for unsupervised super-resolution using mixture models of point and smooth sources applied to astrophysical map-making

Inverse Problems

Volumn 28, Issue 12, 2012, Pages

  Ayasso, Hacheme ^a   Rodet, Thomas ^b   Abergel, Alain ^a  

^a UNIVERSITÉ PARIS SUD   (France)

^b UNIV PARIS SUD   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; DATA SETS; FORM PARAMETERS; GRADIENT-LIKE; HERSCHEL SPACE OBSERVATORIES; IMAGE SUPER-RESOLUTION; MARGINALS; MARKOVIAN MODEL; MIXTURE MODEL; NOISE PARAMETERS; OPTIMAL VALUES; PARAMETRIC FORMS; POINT SOURCES; POSTERIOR MEANS; SUPER RESOLUTION; T-DISTRIBUTION; UNSUPERVISED METHOD; VARIATIONAL BAYESIAN;

BAYESIAN NETWORKS; MAPPING; MARKOV PROCESSES; OPTICAL RESOLVING POWER;

SOURCE SEPARATION;

EID: 84870396385     PISSN: 02665611     EISSN: 13616420     Source Type: Journal    
DOI: 10.1088/0266-5611/28/12/125005     Document Type: Article

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