Seismic data are typically irregularly and sparsely sampled along the spatial coordinates, leading to suboptimal further processing. Matching pursuit Fourier interpolation (MPFI) is a beyond-aliasing interpolation technique for singlecomponent seismic data. The antialiasing capabilities of the method can be improved by using priors, which are typically derived from the lower frequencies in the data, and used to dealias the higher frequencies. In this paper we investigate using a prior derived from a separate, more densely sampled data set. Practical examples are denseover/ sparse-under data and time-lapse data. Tests are done by decimating an existing dataset, deriving the prior from the non-decimated data, and using the priors for the interpolation of the decimated data. It is shown that using priors from a second data set can give a significant uplift in data reconstruction compared with deriving the priors in a conventional way. In particular, some steeply dipping diffraction events are reconstructed better, and a reduction of artifacts is observed.