  Data interpolation is a fundamental step in any seismic processing workflow.
Among machine learning techniques recently proposed to solve data interpolation
as an inverse problem, Deep Prior paradigm aims at employing a convolutional
neural network to capture priors on the data in order to regularize the
inversion. However, this technique lacks of reconstruction precision when
interpolating highly decimated data due to the presence of aliasing. In this
work, we propose to improve Deep Prior inversion by adding a directional
Laplacian as regularization term to the problem. This regularizer drives the
optimization towards solutions that honor the slopes estimated from the
interpolated data low frequencies. We provide some numerical examples to
showcase the methodology devised in this manuscript, showing that our results
are less prone to aliasing also in presence of noisy and corrupted data.
