基于双尺度分解与随机游走的多聚焦图像融合方法

Multi-Focus Image Fusion Method Based on Double-Scale Decomposition and Random Walk

为了较好地平滑边缘区域,避免边缘衔接处产生伪影,提出了一种基于双尺度分解与随机游走的多聚焦图像融合方法。首先,将源图像利用高斯滤波分解为大尺度与小尺度聚焦图,对分解得到的大尺度和小尺度聚焦图采用不同的引导滤波对其边缘进行平滑;然后,将大尺度与小尺度聚焦图作为随机游走算法的标记节点通过融合算法得到初始决策图,并再次使用引导滤波对决策图进行优化;最后,根据决策图对源图像进行重构,得到最终融合图像。实验结果表明,所提方法较好地获取了源图像中的聚焦信息,更好地保留了聚焦区域的边缘纹理及细节信息,在主观评价和客观评价指标方面均取得了更优的效果。

This paper proposes a multi-focus image fusion method based on double-scale decomposition and random walk to smooth the edge region and avoid artifacts at the edge junction.The source images are first decomposed into large-scale and small-scale focus images using a Gaussian filter,and the edges of the decomposed large-scale and small-scale focus images are smoothed using various guiding filters.Then,the large-scale and small-scale focus maps are used as the marker nodes of the random walk algorithm,the initial decision map is obtained using the fusion algorithm,and the guided filter is used to optimize the decision map again.Finally,the source images are reconstructed using the decision graphs to produce the final fused image.The results of the experiments show that our method can effectively obtain the focus information in the source images while retaining the edge texture and detailed information of the focus area.It outperformed the competition in both subjective and objective evaluation indicators.