Robust Statistical Methods for Empirical Software Engineering

Face Super Resolution (FSR) is to infer High Resolution (HR) facial images from given Low Resolution (LR) ones with the assistance of LR and HR training pairs. Among existing methods, Neighbor Embedding(NE) FSR methods are superior in visual and objective quality than holistic based methods. These NE methods are based on the consistency assumption that the neighbors in HR/LR space form similar local geometry. But when LR images are in Very Low Quality (VLQ), the LR patches are seriously contaminated that even two distinct patches form similar appearance, which means that the consistency assumption is not well held anymore. To solve this problem, in this paper we use the target patch as well as the surrounding pixels, which we call parent patch, to represent the target patch. By incorporating the peripheral information, the parent patch is much more robust to noise in the LR and HR consistency learning. The effectiveness of proposed method is verified both quantitatively and qualitatively. In this paper, we also discuss the boundary and the paradox of the multi-scaled parent patch prior in NE based FSR framework.