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Single-Frame Super-Resolution via Compressive Sampling on Hybrid Reconstructions

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

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Abstract

It is well known that super-resolution (SR) is a difficult problem, especially the single-frame super-resolution (SFSR). In this paper, we propose a novel SFSR method, called compressive sampling on hybrid reconstructions (CSHR), with high reconstruction quality and relatively low computation cost. It mainly depends on the combination of the results of other SR methods, which are characteristic of high speed and low quality SR results alone. As a result, CSHR inherits the merit of low computation cost. We resample those low quality SR results in DCT domain instead of in pixel domain and regard the similar expansion coefficients as consensus which would be compressively sampled later. In CSHR, obtaining a high resolution image is only to solve a convex optimization program. We use compressed sensing theory to ensure the efficiency of our method. Also, we give some theoretic results. Experimental results show the effectiveness of the proposed method when compared to some state-of-the-art methods.

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Notes

  1. 1.

    http://sipi.usc.edu/database/database.php?volume=misc.

  2. 2.

    More results are shown in https://gist.github.com/Brilliant/7472969d4020599a13d0.

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Acknowledgements

The authors wish to thank the authors of [9, 14] for generously sharing their code and data with them.

This research is supported in part by the Major State Basic Research Development Program of China (973 Program, 2012CB315803), the National Natural Science Foundation of China (61371078), and the Research Fund for the Doctoral Program of Higher Education of China (20130002110051).

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China

    Ji-Ping Zhang, Tao Dai & Shu-Tao Xia

Authors
  1. Ji-Ping Zhang
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  2. Tao Dai
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  3. Shu-Tao Xia
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Corresponding author

Correspondence to Ji-Ping Zhang .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Zhang, JP., Dai, T., Xia, ST. (2015). Single-Frame Super-Resolution via Compressive Sampling on Hybrid Reconstructions. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_69

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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