Advertisement

Example Based Super Resolution Using Fuzzy Clustering and Neighbor Embedding

  • Keerthi A. S. Pillai
  • M. Wilscy
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

A fuzzy clustering based neighbor embedding technique for single image super resolution is presented in this paper. In this method, clustering information for low-resolution (LR) patches is learnt by Fuzzy K-Means clustering. Then by utilize the membership degree of each LR patch, a neighbor embedding technique is employed to estimate high resolution patches corresponding to LR patches. The experimental results show that the proposed method is very flexible and gives good results compared with other methods which use neighbor embedding.

Keywords

Fuzzy clustering Neighbor embedding Example based super resolution 

References

  1. 1.
    Park S, Park M, Kang MG (2003) Super-resolution image reconstruction, a technical overview. IEEE Signal Process Mag 20(5):21–36MathSciNetCrossRefGoogle Scholar
  2. 2.
    Huang TS, Tsai RY (1984) Multi-frame image restoration and registration. Adv Comput Vis Image Process 1:317–339Google Scholar
  3. 3.
    Elad M, Feuer A (1997) Restoration of a single super resolution image from several blurred, noisy, and under sampled measured images. IEEE Trans Image Process 6(12):1646–1658CrossRefGoogle Scholar
  4. 4.
    Freeman WT, Pasztor EC, Carmichael OT (2000) Learning low-level vision. Int J Comput Vis 40(1):25–47MATHCrossRefGoogle Scholar
  5. 5.
    Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65CrossRefGoogle Scholar
  6. 6.
    Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp 275–282Google Scholar
  7. 7.
    Geng B, Taoet D et al (2009) Ensemble manifold regularization. In: Proceedings of IEEE Computer Society Conference Computer Vision Pattern Recognition Workshops, CVPR Workshops, pp 2396–2402Google Scholar
  8. 8.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  9. 9.
    Li X, Lin S, Yan S, Xu D (2008) Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst Man Cybern B 38(2):342–352Google Scholar
  10. 10.
    Zhou T, Tao D, Wu X (2010) Manifold elastic net: a unified frame-work for sparse dimension reduction. Data Mining Knowl Discov 22(3):340–371MathSciNetCrossRefGoogle Scholar
  11. 11.
    Chan TM, Zhang J, Pu J, Huang H (2009) Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recogn Lett 30(5):494–502CrossRefGoogle Scholar
  12. 12.
    Zhang K, Tao D et al (2011) Partially supervised neighbor embedding for example-based image super-resolution. IEEE J Selected Topics Sig Process 5(2)Google Scholar
  13. 13.
    Bouman CA (2005) Cluster: an unsupervised algorithm for modeling Gaussian mixtures. Purdue Univ West Lafayette, IN (On-line)Google Scholar
  14. 14.
    Zeng H, Cheung Y (2009) A new feature selection method for Gaussian mixture clustering. Pattern Recogn 42(2):243–250MATHCrossRefGoogle Scholar
  15. 15.
    Hoppner F, Klawonn F, Kruse R (1999) Fuzzy cluster analysis: methods for classification. Data analysis and image recognition. Wiley, New YorkGoogle Scholar
  16. 16.
    Krishnapuram R, Frigui H, Nasraoui O (1995) Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation—part I and II. IEEE Trans Fuzzy Syst 3(4):29–60CrossRefGoogle Scholar
  17. 17.
    Hoppner F (1997) Fuzzy shell clustering algorithms in image processing: fuzzyc-rectangular and 2-rectangular shells. IEEE Trans Fuzzy Syst 5(4):599–613CrossRefGoogle Scholar
  18. 18.
    Ruspini ER (1969) A new approach to clustering. Info Control 19:22–32CrossRefGoogle Scholar
  19. 19.
    Bezdek JC (1980) A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2:1–8MATHCrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of KeralaThiruvananthapuramIndia

Personalised recommendations