基于非采样Contourlet变换的图像融合算法

Paizhong Zhang, Lixia Wang, T. Hou
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引用次数: 0

摘要

提出了一种基于非采样Contourlet变换的多传感器图像融合方法。该变换具有平移不变性,适合表达具有丰富细节信息和方向信息的图像,避免了一般方法对融合图像产生的环形效应。通过变换对图像进行分解,得到高、低频分量。高频分量采用PCNN作为融合规则,低频分量采用黄金分割法搜索最优低频融合权值,自适应融合多传感器图像的低频子带系数。对比实验结果表明,该方法能取得较好的融合效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Image Fusion Algorithm based on Nonsampled Contourlet Transform
In this paper, a multisensor image fusion method based on non-sampling Contourlet transform is proposed. The transform has translation invariance, is suitable for expressing images with rich detail information and direction information, and can avoid the ringing effect introduced by general methods to the fused images. The image is decomposed by the transform to obtain high and low frequency components. The high-frequency component uses PCNN as the fusion rule, and the low-frequency component uses the golden section method to search the optimal low-frequency fusion weight, so as to fuse the low-frequency subband coefficients of multi-sensor images adaptively. Comparative experimental results show that the proposed method can achieve better fusion effect.
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