Advertisement

Multimedia Tools and Applications

, Volume 75, Issue 7, pp 4115–4128 | Cite as

Image super-resolution base on multi-kernel regression

  • Jianmin Li
  • Yanyun QuEmail author
  • Cuihua Li
  • Yuan Xie
Article

Abstract

In this paper, a novel approach to single image super-resolution based on the multi-kernel regression is presented. This approach focuses on learning the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid demanding and time-consuming cross validation for kernel selection, we propose multi-kernel regression (MKR) model for image Super-Resolution (SR). Considering the multi-kernel regression model is prohibited when the training data is large-scale, we further propose a prototype MKR algorithm which can reduce the computational complexity. Extensive experimental results demonstrate that our approach is effective and achieves a high quality performance in comparison with other super-resolution methods.

Keywords

Super resolution Kernel regression Multi kernel learning 

Notes

Acknowledgments

This research work is support by the National Natural Science Foundation of China Under Grant No. 61373077 and Grant No.61402480, the Natural Science Foundation of Fujian Province of China Under Grant No. 2013J01257, and the Scientific Research Foundation for the Introduction of Talent at Xiamen University of Technology No. YKJ12023R.

References

  1. 1.
    Chang H, Yeung D-Y, Xiong Y (2004) Super-resolution through neighbor embedding, in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 I.E. Computer Society Conference on:I-IGoogle Scholar
  2. 2.
    Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22:56–65CrossRefGoogle Scholar
  3. 3.
    Freeman WT, Pasztor EC, Carmichael OT (2000) Learning low-level vision. Int J Comput Vis 40:25–47CrossRefzbMATHGoogle Scholar
  4. 4.
    Han Y, Wu F, Tian Q et al (2012) Image annotation by input–output structural grouping sparsity. IEEE Trans Image Process 21(6):3066–3079MathSciNetCrossRefGoogle Scholar
  5. 5.
    Han Y, Yang Y, Ma Z et al (2014) Image attribute adaptation. IEEE Trans Multimedia 16(4):1115–1126CrossRefGoogle Scholar
  6. 6.
    Han Y, Yang Y, Yan Y et al (2015) Semisupervised feature selection via spline regression for video semantic recognition. IEEE Trans Neural Netw Learn Syst 26(2):252–264CrossRefGoogle Scholar
  7. 7.
    Hou HS, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 26:508–517CrossRefzbMATHGoogle Scholar
  8. 8.
    Irani M, Peleg S (1991) Improving resolution by image registration. Graph Models Image Process 53:231–239CrossRefGoogle Scholar
  9. 9.
    Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32:1127–1133MathSciNetCrossRefGoogle Scholar
  10. 10.
    Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10:1521–1527CrossRefGoogle Scholar
  11. 11.
    Lin Z, Shum H-Y (2004) Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell 26:83–97CrossRefGoogle Scholar
  12. 12.
    Ni KS, Nguyen TQ (2007) Image superresolution using support vector regression. IEEE Trans Image Process 16:1596–1610MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sun J, Xu Z, Shum H-Y (2008) Image super-resolution using gradient profile prior, in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conf:1–8Google Scholar
  14. 14.
    Timofte R, De V, Gool LV (2013) Anchored neighborhood regression for fast example-based super-resolution, in Computer Vision (ICCV), 2013 I.E. International Conference on:1920–1927Google Scholar
  15. 15.
    Vedaldi A, Gulshan V, Varma M, Zisserman A (2009) Multiple kernels for object detection, in Computer Vision, 2009 I.E. 12th International Conference on:606–613Google Scholar
  16. 16.
    Yang J, Wright J, Huang TS, Ma Y (2008) Image super-resolution as sparse representation of raw image patches, in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conf:1–8Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Computer Science DepartmentXiamen UniversityXiamenPeople’s Republic of China
  2. 2.The State Key Lab of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations