Sparse representation based face image super-resolution

Authors:
Guangwei Gao;Jian Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, P.R. China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

Generating a high resolution (HR) image from its corresponding low resolution (LR) counterpart is an important problem in many application fields. The recently widely used sparse representation (SR) techniques provide a pioneer work to this inverse problem by incorporating the sparsity prior into the super-resolution reconstruction process. Motivated by this work, in this paper, we present a new face image super-resolution method using the sparse representation, which first seeks a sparse representation for each low-resolution input, and then the representation coefficients are directly used to generate the corresponding high-resolution output. The effectiveness of the proposed method is evaluated through the experiments on the benchmark face database, and the experimental results demonstrate that the proposed method can achieve competitive performance compared with other state-of-the-art methods.