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Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making.

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 \post density 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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https://hal.archives-ouvertes.fr/hal-00778082
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Submitted on : Friday, January 18, 2013 - 4:29:09 PM
Last modification on : Tuesday, November 16, 2021 - 4:01:01 AM
Long-term archiving on: : Friday, April 19, 2013 - 4:04:00 AM

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François Orieux, Jean-François Giovannelli, Thomas Rodet, Alain Abergel. Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making.. Astronomy and Astrophysics - A&A, EDP Sciences, 2013, 549, pp.A83. ⟨10.1051/0004-6361/201219950⟩. ⟨hal-00778082⟩

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