  We propose a novel single-image super-resolution approach based on the
geostatistical method of kriging. Kriging is a zero-bias minimum-variance
estimator that performs spatial interpolation based on a weighted average of
known observations. Rather than solving for the kriging weights via the
traditional method of inverting covariance matrices, we propose a supervised
form in which we learn a deep network to generate said weights. We combine the
kriging weight generation and kriging process into a joint network that can be
learned end-to-end. Our network achieves competitive super-resolution results
as other state-of-the-art methods. In addition, since the super-resolution
process follows a known statistical framework, we are able to estimate bias and
variance, something which is rarely possible for other deep networks.
