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Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6889–6911 | Cite as

Adaptive image rational upscaling with local structure as constraints

  • Yang Ning
  • Yifang Liu
  • Yunfeng Zhang
  • Caiming ZhangEmail author
Article
  • 453 Downloads

Abstract

In this paper, we develop a new interpolation fusion model, Adaptive Image Rational Upscaling (AIRU), based on classical rational interpolation. This model can synthetically consider the influence of the surrounding 12 pixels within the current interpolation cell. Considering the limitation of edge direction estimation of conventional edge detection methods, we introduce a new method to quantify the edge direction based on the Principal Component Edge (PCE). Adaptive weights for each triangular patch can be generated based on three coefficients: angle coefficient which can be estimated by PCE, variation coefficient and gray similarity coefficient. PCE can also be used to divide the image into non-smooth and smooth area. AIRU and conventional interpolation are used in these two areas respectively. Furthermore, the model parameter optimization can further improve the interpolation performance. Experimental results demonstrate that the proposed fusion model achieves competitive performance when compared with the state-of-the-arts.

Keywords

AIRU PCE Angle coefficient Variation coefficient Gray similarity coefficient Parameter optimization 

Notes

Acknowledgments

This work is supported by the National Nature Foundation of China (61602277, 61572292, 61332015), NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(U1609218).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yang Ning
    • 1
  • Yifang Liu
    • 6
  • Yunfeng Zhang
    • 4
    • 5
  • Caiming Zhang
    • 2
    • 3
    • 4
    Email author
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.School of SoftwareShandong UniversityJinanChina
  3. 3.Shandong Co-Innovation Center of Future Intelligent ComputingYantaiChina
  4. 4.Shandong Province Key Lab of Digital Media TechnologyShandong University of Finance and EconomicsJinanChina
  5. 5.Department of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina
  6. 6.Department of Computer Science and EngineeringUniversity at BuffaloNew YorkUSA

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