基于邻域统计信息的红外与可见光图像融合

摘要

针对红外图像与可见光图像的融合问题，提出了一种基于邻域统计信息的图像融合新算法。首先对图像进行多尺度分解，得到一系列子带系数，然后针对各子带系数的物理特性，提出了高低频规则不同的图像融合算法。对于图像低频部分，首先定义基于邻域统计信息的目标和场景特征参数，然后设计了加权系数自适应变化的加权平均融合策略；对于图像高频部分，首先定义邻域系数分布特征参数，然后设计了受邻域统计信息调制的系数比较取大融合策略。实验结果表明该算法能够很好地将红外图像与可见光图像进行融合，且融合效果优于其他一些算法。%Aiming at the fusion problem of infrared and visible images with the same scene,a novel fusion algorithm based on neighbor statistic information is proposed.Firstly the source images are multi-scale decomposed,then many subband coefficients are obtained.Fusion methods of different fusion rules at high and low frequency are presented ac-cording to the physical characteristics of each subband coefficient.For the low frequency subband coefficients,the tar-get and scene parameters based on neighbor statistic information are defined,and a weighted average fusion strategy with weighted coefficient adaptive variation is designed.For the high frequency subband coefficients,the neighborhood distribution characteristics parameter is defined,and a fusion strategy of coefficient comparison with neighbor statistic information modulation is designed.The experimental results show that the proposed algorithm can fuse infrared and visible images well.