Learning patch-based anchors for face hallucination
With the goal of increasing the resolution of face images, recent face hallucination methods advance learning techniques which observe training low and high-resolution patches for recovering the output image of interest. Since most existing patch-based face hallucination approaches do not consider the location information of the patches to be hallucinated, the resulting performance might be limited. In this paper, we propose an anchored patch-based hallucination method, which is able to exploit and identify image patches exhibiting structurally and spatially similar information. With these representative anchors observed, improved performance and computation efficiency can be achieved. Experimental results demonstrate that our proposed method achieves satisfactory performance and performs favorably against recent face hallucination approaches.