Robust super-resolution for face images via principle component sparse representation and least squares regression

Face image super-resolution (SR) reconstruction is the problem of inducing a high-resolution (HR) face image from a low-resolution (LR) one. Traditional face SR methods are either sensitive to noise, i.e., local patch based technologies, or lacking facial details, i.e., global face reconstruction, thus could not achieve a satisfying result. In order to overcome these problems, we propose in this paper a novel face SR method. Taking full advantages of Principle Component analysis and Sparse Representation (PCSR), it aims to obtain an accurate and noise robust representation, transforming the image patch to the principle component sparse feature space (PC-SFS). Moreover, in PC-SFS, we try to learn a mapping function between the LR image patches and HR ones through Least Squares Regression. Given a LR patch, we first transform it to the LR PC-SFS by PCSR to obtain the robust and accurate representation, and then project the representation to the HR PC-SFS thus get the target HR patch. Experiments on the frontal faces SR in noise conditions demonstrate our method outperforms state of the art.