Optimized neighbor embeddings for single-image super-resolution

We describe a self-content single-image super-resolution algorithm based on multi-scale neighbor embeddings of small image patches. Given an input low-resolution patch, we gradually expand its size by relying on local geometric similarities of low- and high-resolution patch spaces under small scaling factors. We characterize the local geometry with K-similar patches taken from an exemplar set and we collect exemplar patch pairs from the input image and its appropriately rescaled versions. While ensuring local images compatibility with an optimization on K, we satisfy image smoothness by patch overlapping. We further enforce global consistency through an adaptive back-projection. Our experimental results show better performance on synthesizing natural looking textures and sharp edges with less artifacts when compared to other methods.