应用激光, 2016, 36 (3): 351, 网络出版: 2016-12-06   

NSST域高斯模糊逻辑的图像融合

Fusion of Gaussian Fuzzy Logic on NSST Domain
作者单位
信阳农林学院信息工程学院,河南 信阳 464000
摘要
为了达到对来自不同传感器的源图像融合时保留各自更多的细节信息,同时降低算法复杂度的目的,首先采用了非下采样剪切波变换(Non-subsampled Shearlet Transform, NSST)和改进模糊逻辑的图像融合方法,利用NSST算法源图像分别进行多尺度、多方向稀疏分解,分别得到低频子带系数和高频方向子带系数。其次对低频子带系数采用基于改进的模糊高斯隶属函数的权值平均融合规则,以此来充分利用源图像的优势信息进行互补;对高频子带系数采用区域纹理平滑度和视觉敏感度系数相结合的融合规则;以此来丰富融合图像的细节信息。最后对低频子带融合系数和高频子带融合系数执行NSST逆变换得到最终的融合图像。对此方法进行了理论分析和实验验证,结果表明此融合方法不仅可以保证融合清晰度,同时还可以缩短算法的运行时间。
Abstract
Dept. of Information Engineering,Xinyang Agriculture and Forestry University, Xinyang, Henan 464000, ChinaTo keep more details information extraction and reduce complexity of algorithm when the source image fusion is processing, based on the Non-subsampled Shearlet Transform(NSST)and the Fuzzy Logic. Making use of NSST to decompose source images on multi-direction and multi-scale sparse, low-frequency components and high frequency components are obtained. The fusion method of the Gaussian Fuzzy Logic is adopted in the low frequency subband; the fusion rule for the combination of the regional texture smoothness and visual sensitivity coefficient is used to the high frequency components. Finally, the fusion image is obtained after executed the NSST inverse transformation. Experimental results show that the fusion method can not only guarantee the definition of the fused image, but also shorten the running time of the algorithm.
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潘贇, 赵喜玲. NSST域高斯模糊逻辑的图像融合[J]. 应用激光, 2016, 36(3): 351. Pan Yun, Zhao Xiling. Fusion of Gaussian Fuzzy Logic on NSST Domain[J]. APPLIED LASER, 2016, 36(3): 351.

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