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《Computer Engineering and Applications》 2011-07
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Self-adaptive remote sensing image fusion using dual-tree complex wavelet and Curvelet transform

HE Tongdi1,2,LI Jianwei2 1.Department of Mechanic and Electronic,Hexi University,Zhangye,Gansu 734000,China 2.Key Lab of Optoelectronic Technology and System of State Education Ministry,Chongqing University,Chongqing 400030,China  
Curvelet transform overcomes the weakness of wavelet transform in dealing with high-dimensional signals.It provides a flexible multiresolution,local and directional image expansion and a sparse representation for two-dimensional piecewise smooth signal resembling images.It can satisfy the anisotropy scaling relation for curves,and thus offers a fast and structured curvelet-like decomposition.When curverlet transform is applied to image fusion,the characteristic of original images can be effectively extracted and more important information is preserved.Fusion rules are very important in image fusion algorithms.Because the rule of self-adaptive has advantage in choosing coefficients and the wavelet based dual-tree complex wavelet-Curverlet transform is of multi-scale and multi-direction.A method based on the rule of local energy for image fusion using dual-tree complex wavelet based Curverlet transform is proposed.The Curvelet transform is used to perform a multiscale decomposition of Pan and Mul image,the original images are decomposed low-frequency coefficients and high-frequency coefficients.To low-frequency coefficients,self-adaptive regional energy rule is used,to high-frequency coefficients,regional feature self-adaptive fusion rule is used.The result images are generated by reverse Curvelet transform based on the low-frequency coefficients and high-frequency coefficients.The experimental results show that this method is an effective and feasible algorithm.
【Fund】: 国家自然科学基金No.40671133~~
【CateGory Index】: TP751
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