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
Link To Document :
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