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A novel weight fusion approach for multi-focus image based on NSST transform domain

机译：基于NSST变换域的多焦点图像的一种新型重量融合方法

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摘要

In this paper, a novel weight multi-focus image fusion method based on the non-subsampled shearlet transform (NSST) is proposed. Firstly, the NSST is employed to decompose the source image into low frequency and high frequency sub-band coefficients with different-scale and different-directional; Secondly, the local least root mean square error (RMSE) is used as a new fusion weight for the fusion of low frequency sub-band coefficients; In order to effectively preserve the edges of the image, a novel fusion rule based on edge preserving weight is proposed to fuse high frequency sub-band coefficients. Finally, the fused image can be obtained through the inverse NSST on the fused coefficients. The experimental results show that the fused image not only contains rich details, but also preserves the notable structural features. Compare with the current state-of-the-art fusion methods, it produces better visual effect and quality assessment.
机译：本文提出了一种基于非撤销剪切变换（NSST）的新型重量多焦图像融合方法。 首先，采用NSST以不同刻度和不同方向分析到低频和高频子带系数的源图像; 其次，将局部最小根均方误差（RMSE）用作低频子带系数融合的新熔合重量; 为了有效保留图像的边缘，提出了一种基于边缘保存权重的新型融合规则，用于熔化高频子带系数。 最后，可以通过融合系数上的逆NSST获得融合图像。 实验结果表明，融合图像不仅包含丰富的细节，而且还保留了显着的结构特征。 与目前的最先进的融合方法进行比较，它会产生更好的视觉效果和质量评估。

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