Face image super-resolution through locality-induced support regression

In this paper we propose a novel face image super-resolution (SR) method named Locality-induced Support Regression (LiSR). Given a low-resolution (LR) input patch, we learn a mapping function between the local support LR and high-resolution (HR) patch pairs to predict its HR version. The support can be obtained from the LR or HR patch manifolds, which leads to two varieties of LiSR, namely LR patch guided LiSR (LR-LiSR) and HR patch guided LiSR (HR-LiSR). LR-LiSR directly learns the mapping function between local support LR/HR patch pairs given an input LR patch. As for HR-LiSR, the support and a mapping function will be iteratively learned to update the target HR patch. The key advantages of our proposed framework are two-fold: (1) the strong regularization of “same representation” of prior work [1,2] is relaxed to the same support, and hence much flexibility can be given to the learned mapping function; (2) we define the support in the LR or HR patch manifold space by incorporating the locality constraint, which can well preserve the manifold structure of the training set. Experimental results reported on both simulated LR face images and real-world datasets demonstrate the effectiveness of the