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Face Image Super-Resolution Based on Topology ICA and Sparse Representation

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8814))

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Abstract

In this paper, a new learning-based face super-resolution (SR) algorithm is proposed considering the similarity of topology structure between low-resolution (LR) image and high-resolution (HR) image and the sparseness of cells’ response in visual cortex. Firstly, we obtain coupling dictionary which are corresponding to LR and HR image patch pairs by applying topology ICA. Then, the sparse coefficients of input LR image according to LR dictionary can be got based on sparse representation theory. Furthermore, primary HR face image is reconstructed using HR dictionary. Finally, finer HR face image can be got by back-projection step. Experiments demonstrate the proposed approach can get good SR results in subjective perception and objective evaluation.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Yongtao Liu, Hua Yan, Xiushan Nie & Zhen Liu

Authors
  1. Yongtao Liu
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  2. Hua Yan
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  3. Xiushan Nie
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  4. Zhen Liu
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Corresponding author

Correspondence to Yongtao Liu .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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© 2014 Springer International Publishing Switzerland

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Liu, Y., Yan, H., Nie, X., Liu, Z. (2014). Face Image Super-Resolution Based on Topology ICA and Sparse Representation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-11758-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11757-7

  • Online ISBN: 978-3-319-11758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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