中文摘要：

Curvelet变换克服了小波变换在处理高维信号时的不足，比小波变换具有更好的方向性、较高的逼近精度和更好的稀疏表达性能。因此将Curvelet变换应用于图像融合领域，能更好地提取图像边缘特征，为融合提取更多的特征信息。利用对偶树复小波-Curvelett变换的多尺度和多方向性特征以及自适应融合规则在选取融合系数上的优势，提出了一种基于对偶树复小波-Curvelet变换的自适应遥感图像融合新算法。算法是将全色图像和多光谱图像进行对偶树复小波-Curvelet变换分解后，针对不同的频率域特点选择不同的融合规则，对低频系数选取区域能量的加权系数自适应融合规则，对高频系数特性选用了区域特征自适应的融合规则，最后通过重构得到融合图像。将其他的融合算法和所提算法进行主观和客观的对比，结果表明，基于对偶树复小波-Curvelet变换区域特征自适应的图像融合算法是一种有效可行的图像融合算法。

英文摘要：

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.