Face hallucination based on PCA dictionary pairs

This paper presents a new position-based face hallucination algorithm based on PCA dictionary pairs. The high-resolution (HR) face image is generated in patch-wise, while each patch is hallucinated from a low-resolution (LR) observation with the training patches on the same position of face images. Different from the previous literatures which reconstruct the HR patch with raw position-patches, a set of dictionary pairs are adaptively learned according to the patch location in the proposed algorithm. We joint the LR-HR position-patches together and project the dataset into principal directions by principal component analysis (PCA). The principal components are applied to generate the coupled LR-HR dictionaries. Moreover, the corresponding eigenvalues are also served as a constraint in the reconstruction. Experimental results demonstrate that the proposed approach achieves superior performance when compared with the state-of-the-art algorithms.