This paper aims at modeling an effective activity measurement for focus areas detection in multifocus images.Considering that the existing methods lack efficiency and are not able to deal with mis-registered source images well,we propose a multiple visual features and guided filtering based fast and robust multifocus image fusion method.First,the initial fusion decision map is obtained by detecting the focus area in source images through measuring three visual features,which are contrast saliency,sharpness and structure saliency;then,the final fusion weight map is acquired by optimizing the initial decision map through morphological filtering and guided filtering to make full use of spatial consistency and to resist artifacts;finally,the fused image is obtained by weighted averaging the source images according to optimized weight map.Experimental results show that our method outperforms existing state-of-the-art multifocus image fusion algorithms,in terms of both subjective and objective quality assessments.%为建立一个有效的活跃度测量模型来检测多聚焦图像的聚焦区域,针对多数融合方法效率不高和处理源图像未配准问题的不足,提出一种基于多视觉特征和引导滤波的快速鲁棒多聚焦图像融合方法.首先通过分别测量对比度显著性、清晰度和结构显著性这3个互补的视觉特征对源图像聚焦区域进行检测,获得初始的融合决策图;为了充分利用空间一致性并有效抑制融合结果中伪影的产生,利用形态学滤波和引导滤波对初始决策图进行优化,从而获得最终的融合权重图;最后根据优化的权重图对源图像进行加权融合,获得融合图像.实验结果表明,无论是主观视觉效果还是客观定量评价,该方法均优于一些主流的多聚焦图像融合方法.
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