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Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation

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Abstract

Single image super-resolution is devoted to generating a high-resolution image from a low-resolution one, which has been a research hotspot for its significant applications. A novel method that is totally based on the single input image itself is proposed in this paper. Firstly, a local-feature based interpolation method where both edge pixel property and location information are taken into consideration is presented to obtain a better initialization. Then, a dynamic lightweight database of self-examples is built with the aid of our in-depth study on self-similarity, from which adaptive linear regressions are learned to directly map the low-resolution patch into its high-resolution version. Furthermore, a gradually upscaling strategy accompanied by iterative optimization is employed to enhance the consistency at each step. Even without any external information, extensive experimental comparisons with state-of-the-art methods on standard benchmarks demonstrate the competitive performance of the proposed scheme in both visual effect and objective evaluation.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, China

    Na Ding, Ye-Peng Liu, Lin-Wei Fan & Cai-Ming Zhang

  2. Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264025, China

    Cai-Ming Zhang

  3. Digital Media Research Institute, Shandong University of Finance and Economics, Jinan, 250061, China

    Cai-Ming Zhang

Authors
  1. Na Ding
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  2. Ye-Peng Liu
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  3. Lin-Wei Fan
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  4. Cai-Ming Zhang
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Correspondence to Cai-Ming Zhang.

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Ding, N., Liu, YP., Fan, LW. et al. Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation. J. Comput. Sci. Technol. 34, 537–549 (2019). https://doi.org/10.1007/s11390-019-1925-9

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  • DOI: https://doi.org/10.1007/s11390-019-1925-9

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