Super-Resolution for Face Images Based on Multi-Scale and Multi-Orientation Features

A new learning-based super-resolution algorithm for face images is presented.In the first step,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 locally best matching to predict the best prior.In the second step,the prior is integrated into Bayesian maximum a posteriori （MAP） framework.Finally,steepest descent method is used to obtain the optimal high-resolution face image.The effectiveness of our approach is demonstrated by extensive experimental results with high-quality predicted face images.