15 November 2017 Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106053P (2017) https://doi.org/10.1117/12.2295802
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform,DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.
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Xia-zhu Xie, Xia-zhu Xie, Ya-wei Xu, Ya-wei Xu, "Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106053P (15 November 2017); doi: 10.1117/12.2295802; https://doi.org/10.1117/12.2295802
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