Face Image Superresolution via Locality Preserving Projection and Sparse Coding

Abstract

It is important to enhance the resolution of face images from video surveillance for recognization and other post processing. In this paper, a novel sparse representation based face image superresolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a LR observation. First, it gets a HR-LR dictionary pair for certain input LR patch via position patch clustering and locality preserving projection (LPP). LPP is used to further refine the training samples by taking into account the geometry structure between training patches. It can make the dictionary more expressive. Second, it sparse codes the input patch over LR dictionary by solving L1-regularized least squares optimization. F eature-sign algorithm is used to get the stable solution. Third, it maps the sparse coding coefficitons obtain from the LR dictionary to the HR dictionary, and reconstruct the HR face image patch with the coefficitions and HR dictionary. By integrating the hallucinated HR patches together with overlapped in adjacent, we can obtain the final HR face image. Experiments conducted on CAS-PEAL-R1 database validate the proposed method both in subjective and objective quality.