Locality preserving partial least squares for neighbor embedding-based face hallucination

Abstract

Neighbor embedding-based face hallucination is structured on the assumption that the manifolds formed by low resolution (LR) and high resolution (HR) image patches in two distinct feature spaces have similar local geometry. However, that is not always true. By introducing local information, a novel partial least squares (PLS) method is proposed, called locality preserving PLS (LPPLS), to find a unified feature space where the correlation between LR and HR image patches on that space is maximized. Applying the proposed LPPLS, we learn the joint mapping of LR and HR image patches simultaneously and then map these image patches onto the unified feature space. The k-nearest neighbor searching and the optimal reconstruction weights computing are performed in this unified feature space as well. Experiments show the effectiveness of proposed method.