Fast Facial Image Super-Resolution via Local Linear Transformations for Resource-Limited Applications

Most popular learning-based super-resolution (SR) approaches suffer from complicated learning structures and highly intensive computation, especially in resource-limited applications. We propose a novel frontal facial image SR approach by using multiple local linear transformations to approximate the nonlinear mapping between low-resolution (LR) and high-resolution (HR) images in the pixel domain. We adopt Procrustes analysis to obtain orthogonal matrices representing the learned linear transformations, which cannot only well capture appearance variations in facial patches but also greatly simplify the transformation computation to matrices manipulation. An HR image can be directly reconstructed from a single LR image without need of the large training data, thus avoiding the use of a large redundant LR and HR patch database. Experimental results show that our approach is computationally fast as well the SR quality compares favorably with the state-of-the-art approaches from both subjective and objective evaluations. Besides, our approach is insensitive to the size of training data and robust to a wide range of facial variations like occlusions. More importantly, the proposed method is also much more effective than other comparative methods to reconstruct real-world images captured from the Internet and webcams.