• DocumentCode
    2728256
  • Title

    Multi-spectrum image fusion algorithm based on weighted and improved wavelet transform

  • Author

    Wang, Zhiwen ; Li, Shaoz ; Cai, Qixian ; Su, Songzhi ; Liu, MeiZhen

  • Author_Institution
    Cognitive Sci. Dept., Xiamen Univ., Xiamen, China
  • Volume
    4
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    63
  • Lastpage
    66
  • Abstract
    A multi-spectrum image fusion algorithm with weighted bi-orthogonal self-adaptive wavelet transform is put forward in this paper, which can make up for defects that there are faintness of image details in multi-spectrum image fusion of lower contrast image. The self-adaptive method of wavelet coefficient local model maximum which is weighted is used to fuse the high frequency components and the syncretism adaptive method is also chosen in the course of fusing low frequency coefficient. The capability of multi-spectrum image fusion is evaluated by calculating mean grads of image. The experimental results show that the fusion rule of our proposed method is more effective.
  • Keywords
    image fusion; wavelet transforms; high frequency components; improved wavelet transform; low frequency coefficient; multispectrum image fusion algorithm; self-adaptive method; syncretism adaptive method; wavelet coefficient local model maximum; weighted bi-orthogonal self-adaptive wavelet transform; weighted wavelet transform; Cognitive science; Frequency; Fuses; Image fusion; Information filtering; Information filters; Libraries; Low pass filters; Phase distortion; Wavelet transforms; image fusion; image information entropy; multi-spectrum image; root mean square error; wavelet transform;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5357741
  • Filename
    5357741