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A novel multi-focus image fusion algorithm based on NSST-FRFT

机译:基于NSST-FRFT的新型多焦点图像融合算法

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Non-subsampled Shearlet transform (NSST) has good properties such as multi-scale, multi-direction and shift invariance, but limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) extend the signal analysis to fractional domain, but it is unable to analysis the partial characteristic of signal. Combine the advantages of NSST and FRFT, a novel image fusion algorithm is proposed. Firstly, NSST is applied to the two source images. Secondly, the FRFT is applied to the low-frequency sub-bands coefficients of NSST to acquire image description in fractional domain. Thirdly, fusion rule selecting maximizes of sum-modified-Laplacian is applied to fuse high-frequency sub-bands coefficients and fusion rule of the simple averaging operation is applied to fuse low-frequency coefficients. Finally, the fused image is obtained by the inverse FRFT and inverse NSST. Experimental results show that the proposed method can not only obtain good visual effect, but also improve its objective evaluation criteria.
机译:非下采样Shearlet变换(NSST)具有良好的属性,例如多尺度,多方向和位移不变性,但将信号分析限制在时频域。分数阶傅立叶变换(FRFT)将信号分析扩展到分数域,但无法分析信号的部分特征。结合NSST和FRFT的优点,提出了一种新的图像融合算法。首先,将NSST应用于两个源图像。其次,将FRFT应用于NSST的低频子带系数,以获取分数域中的图像描述。第三,将求和修正Laplacian最大化的融合规则应用于高频子带系数的融合,将简单平均运算的融合规则应用于低频系数的融合。最后,通过反FRFT和反NSST获得融合图像。实验结果表明,该方法不仅可以获得良好的视觉效果,而且可以提高其客观评价标准。

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