Sparse Bayesian Learning of Filters for Efficient Image Expansion

We propose a framework for expanding a given image using an interpolator that is trained
in advance
with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.