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A Synthesis-by-Analysis Network with Applications in Image Super-Resolution

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Advances in Computer Graphics (CGI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11542))

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Abstract

Recent studies have demonstrated the successful application of convolutional neural networks in single image super-resolution. In this paper, we present a general synthesis-by-analysis network for super-resolving a low-resolution image. Unlike Laplacian Pyramid Super-Resolution Network (LapSRN) that progressively reconstructs the sub-band residuals of high-resolution images, our proposed network breaks through the sequential dependency to expand the input and output into multiple disjoint bandpass signals. At each band, we perform the nonlinear mapping in truncated frequency interval by applying a carefully designed sub-network. Specifically, we propose a validated network sub-structure that considers both efficiency and accuracy. We also perform exhaustive experiments in existing commonly used dataset. The recovered high-resolution image is competitive or even superior in quality compared to those images produced by other methods.

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Acknowledgements

This research work was supported partially by National Key R&D Program of China under grant No. 2017YFB1002703, Natural Science Foundation of China under Grant No. U1736109 and 863 Program of China under Grant No. 2015AA016404.

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Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, People’s Republic of China

    Lechao Cheng & Zhangye Wang

Authors
  1. Lechao Cheng
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  2. Zhangye Wang
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Corresponding author

Correspondence to Zhangye Wang .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

  2. Bournemouth University, Poole, UK

    Jian Chang

  3. University of Geneva, MIRALab, Carouge, Switzerland

    Nadia Magnenat Thalmann

  4. International Christian University, Tokyo, Japan

    Eckhard Hitzer

  5. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

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Cheng, L., Wang, Z. (2019). A Synthesis-by-Analysis Network with Applications in Image Super-Resolution. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_42

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  • DOI: https://doi.org/10.1007/978-3-030-22514-8_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22513-1

  • Online ISBN: 978-3-030-22514-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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