Optical Flow Based Face Hallucination Via Weightedly-Constrained Representation

Abstract

Face hallucination can improve the resolution of observed low-resolution (LR) face image to predict the high-resolution (HR) face image. In order to achieve good result performance, the training samples and local structure prior of face image are utilized by some approaches including Least Square Representation (LSR) and convex optimization to obtain the better representation coefficients. However, they do not pay more attention to the relationship between local-pixel structures of HR training samples and input LR face. Thus, the reconstruction coefficients they get are not optimal. Therefore, Optical Flow based face hallucination via weightedly-constrained representation(OFWCR) has been developed in this paper. Compared with LSR and Sparse Representation (SR), our method uses a warping HR training face image strategy to achieve better details from the input LR face. We also take into account the locality constraint in our effective representation scheme to reach locality and sparsity synchronously. Experiments show that our proposed scheme outperforms state-of-the-art approaches in common database.