Fig. 12.
Importance of the modeling of spatial covariances. Comparison between (a) regularized inversions with a model of the nuisance component that accounts for the nonstationary variances but neglect covariances, and (b) REXPACO inversions. Reconstructions are given first with a linear scale, then with a square-root scaling in order to better distinguish the lowest reconstructed values. Data sets: HR 4796A, RY Lupi, SAO 206462 and PDS 70, see Sect. 3.3.1 for observing conditions.
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