Support-driven sparse coding for face hallucination

By incorporating the prior of positions, position patch based face hallucination methods can produce high-quality results and save computation time. Given a low-resolution face image, the key issue of these methods is how to encode the input low-resolution patch. However, due to stability and accuracy issues, the coding approaches proposed so far are not satisfactory. In this paper, we present a novel sparse coding method via exploiting the support information on the coding coefficients. In particular, the support information is characterized by the locality of the image patch manifold, which has been shown to be critical in data representation and analysis. According to the distances between the input patch and bases in the dictionary, we first assign different weights to the coding coefficients and then obtain the coding coefficients by solving a weighted sparse problem. Our proposed method exploits the non-linear manifold structure of patch samples and the sparse property of the redundant data, leading to stable and accurate representation. Experiments on commonly used databases demonstrate that our method outperforms state of the art.