Single image super-resolution via subspace projection and neighbor embedding

In this paper, we present a novel learning-based single image super-resolution algorithm to address the problems of inefficient learning and improper estimation in coping with nonlinear high-dimensional feature data. Our method named as subspace projection and neighbor embedding (SPNE) first projects the high-dimensional data into two different subspaces respectively, i.e., kernel principal component analysis (KPCA) subspace and modified locality preserving projection (MLPP) subspace to obtain the global and local structures of data. In an optimal low-dimensional feature space, the k-nearest neighbors of each input low-resolution (LR) image patch can be found for efficient learning. Then within similarity measures and proportional factors, the k embedding weights are used to estimate high-frequency information from a training dataset. Finally, we apply iterative back projection (IBP) to further enhance the super-resolution results. Experiments on simulative and actual LR images demonstrate that the proposed approach outperforms the existing NE-based super-resolution methods in terms of visual quality and some selected objective metrics.