基于加权聚焦度的多焦点图像融合算法

为保留更多的源图像信息特征,克服基于梯度等融合算法容易产生伪影的缺陷,提出一种基于加权聚焦度的图像融合算法.计算每个源图像的聚焦度量值空间频率、改进的修正拉普拉斯算子和和梯度加权和,得到聚焦状态图 MSF、Mi-SML和 MSoG,根据隶属函数求出相应隶属度值μSF(z)、μi-SML(z)和μSoG(z),得到各源图像的权重,根据线性归一法生成最终的融合图像,并将该算法与当前几种典型图像融合算法进行主客观评价.结果表明:该算法的标准偏差(σ)高出4.699,平均梯度(G)高出0.382,信息熵(H)也是最高的,其他评价参数也在可接受的范围内,融合图像更清晰,没有产生伪影.

Multi-focal Image Fusion Algorithm Based on Weighted Degree of Focus

In order to preserve more features of source image information and overcome the defect, such as the fusion algorithms based on gradient which is prone to ghosting, this paper proposes a multi-focal image fusion algorithm based on weighted degree of focus. The algorithm calculates the focus metric for each source image with the spatial frequency, the improved operator sum of modified-Laplacian, and the gradient weighting sum, get a focus state images MSF, Mi-SMLand MSoG,then find the corresponding membership valueμSF(z),μi-SML(z)andμSoG(z),according to the membership function of these measures, get the weight of each source image, finally generate the final fusion image according to the linear normal method. The algorithm and the current several typical image fusion algorithms have the subjective and objective evaluation. The results show that the standard deviation (σ) is higher than 4.699, the average gradient (G) is higher than 0.382, the information entropy (H) is the highest, the other evaluation parameters are also within the acceptable range, the fusion image is clearer, and no artifacts are produced.