Modified neighbor embedding-based face hallucination using coupled mappings of partial least squares

Neighbor embedding based face hallucination usually assumes that the two manifolds formed by the small patches in the low-resolution (LR) and corresponding high-resolution (HR) images share the same local geometric structure. However, since there are generally multiple HR images that can be reduced to the same LR image, the assumption does not hold always. Therefore, face hallucination directly based on the assumption may result in blurring and artifacts. To enhance the consistency relationship in face hallucination, we employ partial least squares (PLS) to learn coupled mappings simultaneously and to map the original HR and LR image patches onto a unified feature space, where the distinction between LR and HR image patches pairs is minimized. Then, the k nearest neighbors searching and the computation of optimal reconstruction weights is performed in the unified feature space. Experimental results show that the proposed method outperforms some latest image hallucination algorithms.