A Deep Dictionary Model for Image Super-Resolution

Abstract

Inspired by the recent success of deep neural network architectures and the recent effort to develop multi-layer sparse models, we propose a novel deep dictionary learning architecture which is optimized to address a specific regression task known as single image super-resolution. Contrary to other multi-layer dictionaries, our architecture contains L-1 analysis dictionaries to extract high-level features and one synthesis dictionary which is designed to optimize the regression task. We propose a variation of an existing method to learn the analysis dictionaries and we update them without the need to use a back-propagation approach. Results on image super-resolution are satisfactory.