A real-time example-based single-image super-resolution algorithm via cross-scale high-frequency components self-learning

In this paper, we propose a fast and dictionary-free example-based super-resolution (EBSR) algorithm to solve the contradiction in EBSR methods of their high performance in achieving high visual quality and their low efficiency and high costs. With a novel cross-scale high-frequency components (HFC) self-learning strategy, the missed HFC of a high-resolution (HR) image are approximated from its low-resolution counterparts. A high-quality estimation of the HR image is thus obtained by compensating the HFC to its initial guess. Simulations show that the proposed algorithm gets comparable results to the state-of-the-art EBSR but with much higher efficiency and lower costs.