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Modified Sparse Representation Based Image Super-Resolution Reconstruction

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

A modified sparse representation based image super-resolution reconstruction (ISR) is discussed in this paper. The edge features of high resolution (HR) image patches and the gradient and texture features of low resolution (LR) image patches are considered in our method. Meanwhile, features of LR image patches are classified by extreme learning machine (ELM) classifier. Further, For image patches’ features classified, the fast sparse coding (FSC) algorithm based K-SVD sparse representation is used to train sparse dictionaries. And utilized these dictionaries, LR images can be super-resolution reconstructed well. Simulation results show that our method has clear improvement in visual effect and retain well image detail.

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References

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Acknowledgment

This work was supported by the grants from National Nature Science Foundation of China (Grant No. 61373098 and 61370109), the grant from Natural Science Foundation of Anhui Province (No. 1308085MF85.

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Authors and Affiliations

  1. Department of Communication Technology, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China

    Li Shang & Pin-gang Su

  2. School of Electrical Engineering and Automation, Anhui University, Hefei, 230039, Anhui, China

    Zhan-li Sun

Authors
  1. Li Shang
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  2. Pin-gang Su
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  3. Zhan-li Sun
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Corresponding author

Correspondence to Li Shang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2015 Springer International Publishing Switzerland

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Shang, L., Su, Pg., Sun, Zl. (2015). Modified Sparse Representation Based Image Super-Resolution Reconstruction. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_48

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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