An adaptive learning method for face hallucination using Locality Preserving Projections

The size of training set as well as the usage thereof is an important issue of learning-based super-resolution. In this paper, we presented an adaptive learning method for face hallucination using locality preserving projections (LPP). By virtue of the ability to reveal the non-linear structure hidden in the high-dimensional image space, LPP is an efficient manifold learning method to analyze the local intrinsic features on the manifold of local facial areas. By searching out patches online in the LPP sub-space, which makes the resultant training set tailored to the testing patch, our algorithm performed the adaptive sample selection and then effectively restored the lost high-frequency components of the low-resolution face image by patch-based eigen transformation using the dynamic training set. Finally, experiments fully demonstrated that the proposed method can achieve good performance of super-resolution reconstruction by utilizing a relative small sample.