  Learning-based image super-resolution aims to reconstruct high-frequency (HF)
details from the prior model trained by a set of high- and low-resolution image
patches. In this paper, HF to be estimated is considered as a combination of
two components: main high-frequency (MHF) and residual high-frequency (RHF),
and we propose a novel image super-resolution method via dual-dictionary
learning and sparse representation, which consists of the main dictionary
learning and the residual dictionary learning, to recover MHF and RHF
respectively. Extensive experimental results on test images validate that by
employing the proposed two-layer progressive scheme, more image details can be
recovered and much better results can be achieved than the state-of-the-art
algorithms in terms of both PSNR and visual perception.
