Fully non-local super-resolution via spectral hashing
Super-resolution is the task of creating an high resolution image from a low resolution input sequence. To overcome the difficulties of fine image registration, several methods have been proposed exploiting the non-local intuition, i.e. any datapoint can contribute to the final result if it is relevant. These algorithms however limit in practice the search region for relevant points in order to lower the corresponding computational cost. Furthermore, they define the non-local relations in the high resolution space, where the true images are unknown. In this work, we introduce the use of spectral hashing to efficiently compute fully non-local neighbors. We also restate the superresolution functional using fixed weights in the low resolution space, allowing us to use resolution schemes that avoid many artifacts.