为解决红外图像与可见光图像融合中融合图像质量不高问题, 提出了一种结合量子粒子群优化和邻域统计的图像融合方法。红外和可见光源图像经过多尺度分解成为低频和高频子带。对低频子带, 采用系数加权平均的融合策略, 并通过量子粒子群优化方法搜索最优的融合权值; 对于高频子带, 采用受邻域统计信息调制的系数比较取大融合策略,通过逆变换重构图像得到融合结果。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, 且融合效果优于其它一些算法。

Being aimed at the fusion quality problem of infrared and visible light images with the same scene, a novel fusion algorithm based on Quantum-behaved Particle Swarm Optimization (QPSO) and neighbor statistic features is proposed in this paper. The source images were decomposed with various scales and direction, then many subband coefficients were obtained. For the low frequency subband, the coefficients weighted average fusion strategy was applied, and the optimal weight values were obtained by QPSO. For the high frequency subband, the fusion strategy of coefficients comparison by neighbor statistic feature modulation was proposed. The fusion image was obtained by inverse transform. The experimental results show that the proposed algorithm can fuse infrared and visible images well and acquire better fusion results.