LEARNING LOCAL PIXEL STRUCTURE FOR FACE HALLUCINATION

In this paper, we present a novel learning-based face hallucination method based on the assumption that similar faces will have similar local pixel structures. We use the low-resolution (LR) input face to search a database for K example faces that are the most similar to the input and align them with the input accordingly. The local pixel structures of the target high-resolution (HR) image are learned from those warped HR example faces in a neighbor embedding manner, and a total variation (TV) constraint is employed to aid the learning of all pixels' embedding weights. The learned local pixel structures are then used as constraints to reconstruct a HR version of the input face. Experimental results show that the method performs well in terms of both reconstruction error and visual quality.