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Single Image Super-Resolution Based on Nonlocal Sparse and Low-Rank Regularization

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PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

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Abstract

Image super resolution (SR) is an active research topic to obtain an high resolution (HR) image from the low resolution (LR) observation. Many results of existing methods may be corrupted by some artifacts. In this paper, we propose an SR reconstruction method for single image based on nonlocal sparse and low-rank regularization. We form a matrix for each patch with its vectorized similar patches to utilize the redundancy of similar patches in natural images. This matrix can be decomposed as the low rank component and sparse part, where the low rank component depictures the similarity and the sparse part depictures the fine differences and outliers. The SR result is achieved by the iterative method and corroborated by experimental results, showing that our method outperforms other prevalent methods.

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Acknowledgement

This work is supported by the NSFC (No. 61273298), and Science and Technology Commission of Shanghai Municipality (No. 14DZ2260800).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Chunhong Liu, Faming Fang, Yingying Xu & Chaomin Shen

Authors
  1. Chunhong Liu
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  2. Faming Fang
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  3. Yingying Xu
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  4. Chaomin Shen
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Corresponding author

Correspondence to Chaomin Shen .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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© 2016 Springer International Publishing Switzerland

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Liu, C., Fang, F., Xu, Y., Shen, C. (2016). Single Image Super-Resolution Based on Nonlocal Sparse and Low-Rank Regularization. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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