A multi-scale-based super-resolution method for face image

We propose a new algorithm about multi-scale-based super-resolution on face image. First, steerable pyramid is used to capture low-level local features in face images, and then these features are combined with pyramid-like parent structure and image patch synthetic approach based on neighborhood to predict the best prior. After that, the prior is integrated into Bayesian maximum a posteriori (MAP) framework. Finally, the optimal high-resolution face image is obtained by a global linear smoothing operator. It is can be seen from the experimental result that oriented facial features in the high-resolution face are recovered well. The most crucial is that our algorithm significantly reduces the computational complexity.