A Learning Approach for Single-frame Face Super-resolution

Yu He, Kim-Hui Yap, Lap-Pui Chau

Abstract: This paper presents a new learning approach for single-frame face super-resolution (SR). The aim of face SR is to estimate the missing high-resolution (HR) information from a single low-resolution (LR) face image by learning from training samples in the database. A commonly encountered issue in conventional face SR methods is that when the given LR image is a new face significantly different from those in the database, the quality of the reconstructed HR face is usually unsatisfactory. To alleviate this difficulty, we develop a new method to perform face SR based on principal component analysis (PCA) and locally linear embedding (LLE). The reconstructed HR face is able to preserve standard facial features and detailed local information through a residue prediction method using manifold learning. Experimental results show that the proposed method is effective in performing single-frame face SR.