基于经验模态分解和contourlet变换的图像融合

Youzhi Zheng, Yuli Wu, Hua Zhang
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引用次数: 4

摘要

本文提出了一种基于经验模态分解(EMD)和轮廓波变换(CT)混合表示的图像融合方案,称为EMD-CT分解。EMD- ct分解分为三个阶段:EMD阶段、拉普拉斯金字塔阶段和多向分析阶段。因此,所提出的EMD-CT具有EMD的高自适应性,同时具有CT的多方向分析能力。对于图像融合,将融合规则应用于输入图像的EMD-CT表示,以产生复合表示。通过对复合EMD-CT表示进行反变换得到融合图像。实验结果表明,该融合算法比基于单个EMD或CT的融合算法更有效,融合质量更高,特别是对于边缘和轮廓等方向特征丰富的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image fusion using a hybrid representation of empirical mode decomposition and contourlet transform
This paper proposes an image fusion scheme based on a hybrid representation of empirical mode decomposition (EMD) and contourlet transform (CT), named the EMD-CT decomposition. The EMD-CT decomposition consists of three stages: the EMD stage, the Laplacian pyramid stage, and the multidirection analysis stage. As a result, the proposed EMD-CT shares high adaptivity of the EMD while owning multidirection analysis of the CT. For image fusion, fusion rules are applied on the EMD-CT representations of input images to produce a composite representation. The fused image is obtained by inversely transforming the composite EMD-CT representation. Experimental results show that the proposed fusion algorithm is more effective than fusion algorithms based on individual EMD or CT, and produces high fusion quality, especially for images with rich directional features such as edges and contours.
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