摘要
针对小波变换在图像边缘表达方面的局限性,以及Curvelet变换在表达图像点特征上的不足,提出了在红外图像与可见光图像融合的过程中采用基于Curvelet变换和小波变换相结合的图像融合算法。首先对图像进行Curvelet分解,对低频系数使用基于小波变换的融合算法,对高频系数结合融合图像的特点分别采用了两种不同的选取方法:模值绝对值取大法和基于系数相关性法。最后,对最终系数进行反Curvelet变换,得到融合结果图。采用该算法进行了大量的红外图像与可见光图像融合实验,实验结果表明,此算法的融合结果图获得了更好的目标信息和光谱信息。
In order to overcome the weakness of expression of image edge in wavelet transform and feature points in Curvelet transform, a multifocus image fusion algorithm is proposed, which is based on combination of wavelet and Curvelet transform. Firstly, each of the images is decomposed using Curvelet trasnform, among which the low-frequency coefficients are fused by wavelet-based method, while two different select methods are used in high-frequency ones considering feathers of the image：one method is to select a larger value coefficient, and the other one is based on correlation of the two coefficients. Finally, the fused image is reconstructed by performing the inverse Curvelet transform. A large amount of fusion experiments of infrared images and visible images are carried out by the algorithm. Experiment results indicate that better goal and spectral information are obtained.
出处
《光电子技术》
CAS
北大核心
2010年第2期111-116,共6页
Optoelectronic Technology
