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The extraction of foreground and CMB maps from multi-frequency observationsrelies mostly on the different frequency behavior of the different components.Existing Bayesian methods additionally make use of a Gaussian prior for the CMBwhose correlation structure is described by an unknown angular power spectrum.We argue for the natural extension of this by using non-trivial priors also forthe foreground components. Focusing on diffuse Galactic foregrounds, we proposea log-normal model including unknown spatial correlations within each componentand cross-correlations between the different foreground components. We presentcase studies at low resolution that demonstrate the superior performance ofthis model when compared to an analysis with flat priors for all components.

Bayesian CMB foreground separation with a correlated log-normal model