  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.
