Face hallucination via re-identified K-nearest neighbors embedding

Based on locally linear embedding (LLE) manifold learning theory, which assumes that the low-resolution (LR) manifold and high-resolution (HR) manifold spaces share the same local geometry structure, neighbor embedding based super-resolution(SR) methods search K-nearest neighbors(K-NN) of LR patch, then use the counterpart HR patches to estimate HR patch. The primary issue of these methods is how to search the optimal K-NN. However, due to the “one-to-many” mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is very different with its HR counterpart´s. In this paper, we explore a novel and effective re-identified K-NN(RIKNN) method to search neighbors of LR patch by taking into consideration the neighbor information in the HR space. It searches K-NN of LR patch in the LR space and then refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improvement performance. Experimental results with application to face hallucination demonstrate that our method outperforms state of the art in terms of subjective and objective results and computational complexity.