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中文摘要：
Curvelet变换是继小波变换之后，更适合图像处理的一种新的多尺度变换分析方法，相比小波而言，它更加适合分析二维图像巾的曲线或直线状边缘特征，而且具有更高的逼近精度和更好的稀疏表达能力，同时也具有很强的方向性。本文论述了Curvelet变换的理论和实现算法，基于考虑图像中的那些弱的边缘，提出了一种利用Curvelet变换进行遥感图像融合的方法。实验结果分析表明：将Curvelet变换引入图像融合，能够更好地提取原始图像特征，为融合图像提供更多信息，使融合图像在较好地保留光谱信息的同时，空间细节信息得到增强，优于典型的IHS变换、主分量变换及小波变换图像合方法。
英文摘要：
Curvelet, as a new multi-scale analysis algorithm, is more suitable for image processing than wavelet and more appropriate for analyzing the image edge characteristics of curve and line, and it has better approximation precision and sparsity description. In addition, the representation contains more directional information. In this paper, the theory and implementation of Curvelet transform is reviewed. Then, a novel method of remote sensing image fusion is proposed based on Curvelet transform and the scheme to merge the images is described. The addressed fusion method utilizes mainly the faint edges in original images. The result of fusion experiments indicates that the performance of Curvelet-based image fusion method is prior to those of the methods based on IHS, PCA, and wavelet transform. When Curvelet transform is introduced to image fusion, the characteristics of original images are extracted better, which provides more information for image fusion. It not only preserves the spectral information of original multi- spectral image well, but also enhances spatial detail information greatly. Therefore, the Curvelet-based image fusion method can provide an optimum result.
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