Coupled learning based on singular-values-unique and hog for face hallucination

This paper proposed a novel method for face hallucination based on a neighbor embedding technique. Traditional neighbor embedding approaches often offer counterintuitive results because consistency between high resolution images and low resolution images cannot be preserved without taking the intrinsic features of the image patches into account. In order to reinforce the consistency, on the one hand, we exploit the singular-values-unique (SVU) features inspired by singular values decomposition (SVD) successfully applied in image processing. On the other hand, we introduced the Histograms of Oriented Gradients (HOG) features to characterize the local geometric structure of the image patches to alleviate the effects of noise. At last, the learning space is extended to a coupled feature space that combines the SVU and HOG features. Simulation experiments show that this proposed approach could provide competitive results in simulation experiments in subjective and objective quality.