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A Novel Image Fusion Approach Combined Singular Value Decomposition with Averaging Operation

机译:奇异值分解与平均运算相结合的图像融合新方法

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This paper presents a new image fusion algorithm, combined with business singular value decomposition (QSVD) with a simple average operation. Multi-focused image is the first averaging into a new image. The most error-contributing components in each error image are the most contribution to the portion of the image using QSVD Multi-focused to reduce mistakes. Each reduce error image, put forward a new kind of calculation singular vectors fusion image. Finally get to decide to fill each image fusion image through the calculation standard deviation. The experimental results, such as mutual information (MI), information entropy (IE), maintain edge information (Qabf) to the signal- noise-ratio (SNR) and root mean square error (RMSE) is used to assess algorithm. The experimental results show that the algorithm is a kind of high efficient development fusion algorithm.
机译:本文提出了一种新的图像融合算法,结合了具有简单平均操作的业务奇异值分解(QSVD)。多聚焦图像是第一个平均到新图像中的图像。使用QSVD Multi-focused以减少错误的情况下,每个错误图像中导致错误最多的组件是对图像部分的最大贡献。每次减小误差图像,提出一种新型的计算奇异矢量融合图像。最后通过计算标准差决定填充每个图像融合图像。实验结果包括互信息(MI),信息熵(IE),维持边缘信息(Qabf)到信噪比(SNR)和均方根误差(RMSE)来评估算法。实验结果表明,该算法是一种高效的开发融合算法。

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