2016
Learning Parametric Sparse Models for Image Super-Resolution
NeurIPS 2016poster
Learning accurate prior knowledge of natural images is of great importance for single image super-resolution (SR). Existing SR methods either learn the prior from the low/high-resolution patch pairs or estimate the prior models from the input low-resolution (LR) image. Specifically, high-frequency d…