Generalized joint kernel regression and adaptive dictionary learning for single-image super-resolution

•Adaptive joint kernel regression simultaneously exploits local and nonlocal image priors.•The generalization to multi-scales and rotations encourages better super-resolution results.•Dictionary prior learned online and offline interacts with the regional redundancy adaptively.•A discriminative global face prior based on Partial Least Squares for face super-resolution.•State-of-the-art performance for both generic and face images also with high face recognition rates.

This paper proposes a new approach to single-image super-resolution (SR) based on generalized adaptive joint kernel regression (G-AJKR) and adaptive dictionary learning. The joint regression prior aims to regularize the ill-posed reconstruction problem by exploiting local structural regularity and nonlocal self-similarity of images. It is composed of multiple locally generalized kernel regressors defined over similar patches found in the nonlocal range which are combined, thus simultaneously exploiting both image statistics in a natural manner. Each regression group is then weighted by a regional redundancy measure we propose to control their relative effects of regularization adaptively. This joint regression prior is further generalized to the range of multi-scales and rotations. For robustness, adaptive dictionary learning and dictionary-based sparsity prior are introduced to interact with this prior. We apply the proposed method to both general natural images and human face images (face hallucination), and for the latter we incorporate a new global face prior into SR reconstruction while preserving face discriminativity. In both cases, our method outperforms other related state-of-the-art methods qualitatively and quantitatively. Besides, our face hallucination method also outperforms the others when applied to face recognition applications.