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n10:methodsdataanalysis n10:methodsstatistical n17:numericalmethodsandcodes n10:methodsnumerical n10:techniquesimageprocessing
dcterms:title
Estimating hyperparameters and instrument parameters in regularized inversion Illustration for Herschel/SPIRE map making
dcterms:date
2013-01-01
rdaw:P10072
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dcterms:abstract
We describe regularized methods for image reconstruction and focus on the question of hyperparameter and instrument parameter estimation, i.e. unsupervised and myopic problems. We developed a Bayesian framework that is based on the posteriordensity for all unknown quantities, given the observations. This density is explored by a Markov chain Monte-Carlo sampling technique based on a Gibbs loop and including a Metropolis-Hastings step. The numerical evaluation relies on the SPIRE instrument of the Herschel observatory. Using simulated and real observations, we show that the hyperparameters and instrument parameters are correctly estimated, which opens up many perspectives for imaging in astrophysics.
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aa19950-12
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2013-01-01
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© ESO, 2013
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ESO
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