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.
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Gao, G., Yang, J. (2012). Sparse Representation Based Face Image Super-Resolution. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_39
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DOI: https://doi.org/10.1007/978-3-642-31919-8_39
Publisher Name: Springer, Berlin, Heidelberg
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