External and internal learning for single-image super-resolution

Super-resolution (SR) problem still faces a challenge of wisely utilizing diverse learned priors to recover the lost details in low resolution images. In this work, we propose a novel method using low rank decomposition which integrates diverse priors learned from external and internal learning to construct SR image. The proposed method first applies an external dictionary learning to get the meta-detail that is commonly shared among images, and then introduces an internal prior learning to learn the local self-similarity (local structure) that is shared in the image. Both are essential but different priors for SR image construction. With these priors, a bank of preliminary HR images are obtained but with estimation errors and noise. To restrain the errors and noise, we consider these HR images as a high dimension data in dimension reduction problem, and solve it using a low rank decomposition. Experimental results show the proposed method preserves image details effectively, also outperforms state-of-the-arts in both visual and quantitative assessments, especially in dealing with the noise.