Fusion Algorithm for Infrared and Visible Light Images Based on QPSO and Neighbor Statistic Features
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.
国家科技期刊平台
NSTL
万方数据
维普
