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Multimedia Tools and Applications

, Volume 70, Issue 2, pp 741–755 | Cite as

Image super-resolution based on multi-space sparse representation

  • Guodong Jing
  • Yunhui Shi
  • Dehui Kong
  • Wenpeng Ding
  • Baocai Yin
Article

Abstract

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.

Keywords

Super-resolution Sparse representation Total variation Over-complete bases 

Notes

Acknowledgments

This paper is supported by 973 Program (2011CB302703), NSFC (No. 61033004, 60825203, 60973056, 61170103, U0935004, 61003182), BJNSF (4102009, 4112007).

References

  1. 1.
    Alfonso S, Gonzalo P (2008) Noniterative interpolayion-based super-resolution minimizing aliasing in the reconstructed image. IEEE Trans Image Process 17(10):1817–1826CrossRefMathSciNetGoogle Scholar
  2. 2.
    Baker S, Kanade T (2002) Limits of super-resolution and how to break them. IEEE Trans Pattern Anal Mach Intell 24:1167–1183CrossRefGoogle Scholar
  3. 3.
    Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image impainting. Proceeding ACM SIGGRAPH, New Orleans, LA,(July 2000) pp:417–424Google Scholar
  4. 4.
    Cai JF, Hui J, Liu CQ, Shen ZW (2009) Blind motion deblurring from a single image using sparse approximation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (Jun. 2009)Google Scholar
  5. 5.
    Candes E (2006) Compressive sensing. Proceeding International Congress of Mathematicians, (2006)Google Scholar
  6. 6.
    Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30CrossRefGoogle Scholar
  7. 7.
    Capel D, Zisserman A (2001) Super-resolution from multiple views using learnt image models. Proc IEEE Conf Comput Vis Pattern Recognit 2(II):627–634Google Scholar
  8. 8.
    Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process PP(99):1MathSciNetGoogle Scholar
  9. 9.
    Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745CrossRefMathSciNetGoogle Scholar
  10. 10.
    Herman MA, Strohmer T (2009) High-resolution radar via compressed sensing. IEEE Trans Signal Process 57:2275–2284CrossRefMathSciNetGoogle Scholar
  11. 11.
    Irani M, Peleg S (1991) Improving resolution by image restoration. CVGIP: Graph Models Image Process 53:231–239Google Scholar
  12. 12.
    Landgrebe TCW, Duin RPW (2008) Efficient multi-class ROC approximation by decomposition via confusion matrix perturbation analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol: 30, Issue: 5, (May 2008) pp: 810 vol. 2Google Scholar
  13. 13.
    Lee H, Battle A, Raina R, Ng AY (2006) Efficient sparse coding algorithms. NIPS, (2006)Google Scholar
  14. 14.
    Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527CrossRefGoogle Scholar
  15. 15.
    Rauhut H, Schnass K, Vandergheynst P (2008) Compressed sensing and redundant dictionaries. IEEE Trans Inf Theory 54:2210–2219CrossRefMathSciNetGoogle Scholar
  16. 16.
    Schultz R, Stevenson R (1996) Extraction of highresolution frames from video sequences. IEEE Trans Image Process 5:996–1011CrossRefGoogle Scholar
  17. 17.
    Sun J, Zheng NN, Tao H, Shum HY (2003) Image Hallucination with Primal Sketch Priors. Proc IEEE Conf Comput Vis Pattern Recognit 2(II):729–736Google Scholar
  18. 18.
    Yang JC, John W, Ma Y, Huang T (2008) Image super-resolution as a sparse representation of raw image patches. Proc IEEE Conf Comput Vis Pattern Recognit, Anchorage, Alsk 2008:333–340Google Scholar
  19. 19.
    Yang JC, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19:2861CrossRefMathSciNetGoogle Scholar
  20. 20.
    Yang J, Zhang Y (2009) Alternating direction algorithms for L1-problems in compressive sensing. Rice University CAAM Technical Report, (2009) TR09-37Google Scholar
  21. 21.
    Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238CrossRefGoogle Scholar
  22. 22.
    Zhang X, Wu W (2008) Image interpolation by adaptive 2D autoregressive modeling and soft-decision estimation. IEEE Trans Image Process 17(6):887–896CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Guodong Jing
    • 1
  • Yunhui Shi
    • 1
  • Dehui Kong
    • 1
  • Wenpeng Ding
    • 1
  • Baocai Yin
    • 1
  1. 1.Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology, College of Computer Science and TechnologyBeijing University of TechnologyBeijingChina

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