A Novel Image Fusion Approach Combined Singular Value Decomposition with Averaging Operation

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 208)


This paper presents a new image fusion algorithm, combined with business singular value decomposition (QSVD) with a simple average operation. Multi-focused image is the first averaging into a new image. The most error-contributing components in each error image are the most contribution to the portion of the image using QSVD Multi-focused to reduce mistakes. Each reduce error image, put forward a new kind of calculation singular vectors fusion image. Finally get to decide to fill each image fusion image through the calculation standard deviation. The experimental results, such as mutual information (MI), information entropy (IE), maintain edge information (Qabf) to the signal- noise-ratio (SNR) and root mean square error (RMSE) is used to assess algorithm. The experimental results show that the algorithm is a kind of high efficient development fusion algorithm.


Image fusion Singular value decomposition Averaging operation 


  1. 1.
    Hom RA, Johnson CR (1987) Matrix analysis, vol 35(2). Cambridge University Press, Cambridge p 77Google Scholar
  2. 2.
    Golub GH, Van Loan CF (1996) Matrix computation, vol 67(5) 3rd ed. John Hopkins University Press, Baltimore pp 110–118Google Scholar
  3. 3.
    Paige CC (1986) Computing the generalized singular value decomposition. Siam J Sc Stat Comp 7(4):126–146Google Scholar
  4. 4.
    Paige CC, Saunders MA (1981) Towards a generalized singular value decomposition. Siam J Num Anall 8(3):198–405Google Scholar
  5. 5.
    Betcke T (2008) The Generalized singular value decomposition and the method of particular solutions. Siam J Sci Comput 30(3):1278–1295MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Hossny N, Nahavandi S, Creighton D (2008) Comments on “Information measure for performance of image fusion”. Electron Lett 44(8):1066–1067CrossRefGoogle Scholar
  7. 7.
    Xydeas CS, Petrovic V (2000) Objective image fusion performance. Electron Lett 36(3):308–309CrossRefGoogle Scholar
  8. 8.
    Sun YQ, Li LP (2010) Research on wavelet base selection in infrared image fusion. J Comput Info Syst 6(1):2823–2831Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Computer and Software AcademeZhongyuan Institute of TechnologyZhengzhouChina

Personalised recommendations