基于分数阶微分的多聚焦图像融合

摘要/Abstract

摘要： 多聚焦图像融合利用图像的众多互补信息,获取清晰的融合图像。在传统的基于多尺度分析方法采样与融合策略容易造成图像信息丢失;基于稀疏表示方法,往往因字典表达能力不足,导致融合细节模糊,且融合时间复杂度非常高。在基于空域法的多聚焦图像融合方法中,度量图像活跃度的算法十分关键。文中提出利用分数阶微分特征来度量图像的活跃度。该算法首先用8个方向的分数阶模板对图像进行卷积,累加每个方向卷积后的绝对值,得到原始图像的活跃度量图;然后利用滑动窗口技术分别对每个度量图进行比较,窗口内累加和大的被视为聚焦且得分图加1,以得分图信息得到决策图;最后通过决策图对原始图像加权的方式得到最终融合图像。实验对比分析表明,此算法相比传统算法具有一定的优越性。

关键词: 多聚焦图像融合, 分数阶微分, 滑动窗口, 活跃度

Abstract: Multi-focus image fusion uses many complementary information of the image to obtain a clear fused image.In traditional multi-scale analysis methods,image information is easily lost due to sampling and fusion strategies.In sparse representation methods,due to the lack of dictionary expression ability,the fusion details are blurred and the fusion time complexity is very high.For multi-focus image fusion method based on spatial domain method,the algorithm for measuring image activity level is very critical.A fractional differential feature is proposed to measure the activity level of the image.The algorithm first convolves the image with a fractional mask in eight directions,and then accumulates the absolute value after convolution in each direction to obtain the activity level measurement of the original image.Each metric map is then compared separately by using a sliding window technique.The sum of the windows and the large ones is regarded as the focus,and the corresponding score map is incremented by one.The decision map is obtained by the score map information.Finally,the final fused image is obtained by weighting the original image by decision graph.Through experimental comparison and analysis,this algorithm has certain advantages over traditional algorithm.

Key words: Activity level, Fractional differential, Multi-focus image fusion, Sliding window

中图分类号:

TP391

毛义坪, 余磊, 官泽瑾. 基于分数阶微分的多聚焦图像融合[J]. 计算机科学, 2019, 46(11A): 315-319. https://doi.org/

MAO Yi-ping, YU Lei, GUAN Ze-jin. Multi-focus Image Fusion Based on Fractional Differential[J]. Computer Science, 2019, 46(11A): 315-319. https://doi.org/

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