Learning color image expansion filters

Image expansion by linear filtering is attractive and widely used because of its simplicity and efficiency, and many interpolation methods fall in this category. In this study, we model filtering as linear regression from low- to high-resolution color image patches, and propose a learning-based design method of image expansion filters based on sparse Bayesian estimation. Sparseness is imposed on the filter coefficients to obtain compact supports. Image expansion is formulated as the problem of finding the predictive mean of a high-resolution patch given a low-resolution patch to expand. Since an exact evaluation of the predictive distribution is difficult, variational methods are employed to derive an efficient algorithm. Experiments on test data show that good generalization performance is obtained based on sparse filters and that color modeling improves the expansion quality.