  Standard interpolation techniques are implicitly based on the assumption that
the signal lies on a homogeneous domain. In this letter, the proposed
interpolation method instead exploits prior information about domain
inhomogeneity, characterized by different, potentially overlapping, subdomains.
By introducing a domain-similarity metric for each sample, the interpolation
process is then based on a domain-informed consistency principle. We illustrate
and demonstrate the feasibility of domain-informed linear interpolation in 1D,
and also, on a real fMRI image in 2D. The results show the benefit of
incorporating domain knowledge so that, for example, sharp domain boundaries
can be recovered by the interpolation, if such information is available.
