Skip to main content
SpringerLink
Log in

Technique for image fusion based on non-subsampled contourlet transform domain improved NMF

  • Research Papers
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

To overcome the problem of multi-sensor image fusion, a technique for image fusion based on non-subsampled contourlet transform (NSCT) domain improved nonnegative matrix factorization (NMF) is presented. Firstly, by using NSCT, multi-scale and multi-direction sparse decompositions of source images are performed. Then, an improved NMF technique is utilized to complete the fusion of low-frequency sub-images. The low-frequency fused image can be produced fast by the process which does not involve the randomization of the vectors W and H at all, in addition, the fusion course of high-frequency sub-images can be dealt with by use of the model of adaptive unit-fast-linking pulse coupled neural network (AUFLPCNN). Finally, the ultimate fused image can be obtained by synthesizing all sub-images with inverse NSCT. The simulated experiments show that the technique is effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Yang W, Chen J, Matsushita B, et al. Practical image fusion method based on spectral mixture analysis. Sci China Inf Sci, 2010, 53: 1277–1286

    Article  Google Scholar 

  2. Lee D D, Seung H S. Learning the parts of objects with nonnegative matrix factorization. Nature, 1999, 401: 788–791

    Article  Google Scholar 

  3. Lee D D, Seung H S. Algorithms for nonnegative matrix factorization. Adv Neural Inf Process Syst, 2001, 13: 556–562

    Google Scholar 

  4. Wang Y, Jia Y, Hu C, et al. Non-negative matrix factorization framework for face recognition. Int J Patt Recogn, 2005, 19: 495–511

    Article  Google Scholar 

  5. Spratling M W. Learning image components for object recognition. J Mach Learn Res, 2006, 7: 793–815

    MathSciNet  Google Scholar 

  6. Liu W X, Zheng N N, You Q B. Nonnegative matrix factorization and its applications in pattern recognition. Chin Sci Bull, 2006, 51: 241–250

    Google Scholar 

  7. Liu W X, Yuan K H, Ye D T. Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis. J Biomed Inform, 2008, 41: 602–606

    Article  Google Scholar 

  8. Wang W, Guan X H, Zhang X L. Profiling program and user behaviors for anomaly intrusion detection based on nonnegative matrix factorization. In: IEEE 43rd conference on decision and control. Atlantis, Paradise Island, Bahamas: IEEE Press, 2004. 657–662

    Google Scholar 

  9. Guan X H, Wang W, Zhang X L. Fast intrusion detection based on a non-negative matrix factorization model. J Netw Comput Appl, 2009, 32: 31–44

    Article  Google Scholar 

  10. Zhang F B, Yang H. Application of non-negative matrix factorization on intrusion detection. J Harbin Univ Sci Tech, 2008, 13: 19–22

    Google Scholar 

  11. Miao Q G, Wang B S. A novel algorithm of multi-Sensor image fusion using non-negative matrix factorization. J Comput aid Design Comput Graph, 2005, 17: 2029–2032

    Google Scholar 

  12. Miao Q G, Wang B S. Multi-focus image fusion based on non-negative matrix factorization. Acta Opt Sin, 2005, 25: 755–759

    Google Scholar 

  13. Wang Z N, Yu X C, Zhang L B. A remote sensing image fusion algorithm based on non-negative matrix factorization. J Beijing Norm Univ (Nat Sci), 2008, 44: 387–390

    Google Scholar 

  14. Chen J. Image fusion method research based on wavelet and WNMF. Dissertation for the Ph.D. Degree. Wuhan: Wuhan University of Technology, 2009

    Google Scholar 

  15. Li S Z, Hou X W, Zhang H J. Learning spatially localized, parts-based representation. In: Proceedings of Computer Vision Pattern Recognition. Kauai, USA: Springer Press, 2001. 207–212

    Google Scholar 

  16. Buciu I, Pitas I. NMF, LNMF, and DNMF modeling of neural receptive fields involved in human facial expression perception. J Vis Commun Image R, 2006, 17: 958–969

    Article  Google Scholar 

  17. Oh H J, Lee K M, Lee U S, et al. Occlusion invariant face recognition using selective LNMF basis images. Lect Notes Comput Sci, 2006, 3852: 120–129

    Article  Google Scholar 

  18. Hoyer P. Non-negative matrix factorization with sparseness constraints. J Mach Learn Res, 2004, 5: 1457–1469

    MathSciNet  Google Scholar 

  19. Gao Y, Church G. Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics, 2005, 21: 3970–3975

    Article  Google Scholar 

  20. O’Grady P D, Pearlmutter B A. Convolutive non-negative matrix factorization with sparseness constraint. In: Conference on Machine Learning for Signal Processing. Mayo, Ireland, 2006. 427–432

  21. Bajla I, Soukup D. Non-negative matrix factorization—A study on influence of matrix sparseness and subspace distance metrics on image object recognition. In: Conference on Quality Control by Artificial Vision, Le Creusot, France, 2007. 653614

  22. Samko O, Rosin P L, Marshall A D. Robust automatic data decomposition using a modified sparse NMF. Lect Notes Comput Sci, 2007, 4418: 225–234

    Article  Google Scholar 

  23. O’Grady P D, Pearlmutter B A. Discovering speech phones using convolutive non-negative matrix factorization with a sparseness constraint. Neurocomputing, 2008, 72: 88–101

    Article  Google Scholar 

  24. Guillamet D, Bressan M, Vitria J. A weighted non-negative matrix factorization for local representations. In: Proceedings of Computer Vision Pattern Recognition, 2001. 942–947

  25. Guillamet D, Vitria J. Evaluation of distance metrics for recognition based on non-negative matrix factorization. Patt Recogn Lett, 2003, 24: 1599–1605

    Article  MATH  Google Scholar 

  26. Guillamet D, Vitria J, Scheile B. Introducing a weighted non-negative matrix factorization for image classification. Patt Recogn Lett, 2003, 24: 2447–2454

    Article  MATH  Google Scholar 

  27. Kong W W, Lei Y J, Lei Y, et al. Image fusion technique based on NSCT and adaptive unit-fast-linking PCNN. IET Image Process, 2010 (in press)

  28. Wang D, Zhang L, Wu Y. The structured total least squares algorithm research for passive location based on angle information. Sci China Ser F-Inf Sci, 2009, 52: 1043–1054

    Article  MATH  MathSciNet  Google Scholar 

  29. Klema V C. The singular value decomposition: its computation and some application. IEEE Trans Automat Contr, 1980, 25: 164–176

    Article  MATH  MathSciNet  Google Scholar 

  30. Konsstantinides K, Yao K. Statistical analysis of effective singular values in matrix rank determination. IEEE Trans Acoustics Speech Signal Process, 1988, 36: 757–763

    Article  Google Scholar 

  31. Boutsidis C, Gallopoulos E. SVD based initialization: A head start for nonnegative matrix factorization. Patt Recogn, 2008, 41: 1350–1362

    Article  MATH  Google Scholar 

  32. Miao Q G. Research on multi-sensor image fusion methods. Dissertation for the Ph.D. Degree. Xi’an: Xidian University, 2005

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Missile Institute, Air Force Engineering University, Xi’an, 713800, China

    WeiWei Kong, YingJie Lei & Yang Lei

  2. Department of Computer, Qinghai University, Xining, 810016, China

    Jie Zhang

Authors
  1. WeiWei Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. YingJie Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to WeiWei Kong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kong, W., Lei, Y., Lei, Y. et al. Technique for image fusion based on non-subsampled contourlet transform domain improved NMF. Sci. China Inf. Sci. 53, 2429–2440 (2010). https://doi.org/10.1007/s11432-010-4118-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-010-4118-2

Keywords

Search

Navigation