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改进多目标粒子群优化算法及在图像融合中的应用
引用本文:李娟,南旭良,毕思远,吴微.改进多目标粒子群优化算法及在图像融合中的应用[J].吉林大学学报(工学版),2013(Z1):477-480.
作者姓名:李娟  南旭良  毕思远  吴微
作者单位:吉林大学通信工程学院;清华大学电子工程系
基金项目:吉林大学基本科研业务费项目(201103214)
摘    要:在深入研究图像融合算法的基础上,受多目标粒子群优化算法(MOPSO)的启发,提出了一种改进的MOPSO算法,并将该改进算法用于图像融合方面。这种算法提出了两次调节指数收敛函数,使得寻优速率得到更为平滑地过渡,从而让搜索结果更好的接近Pareto最优解集。实验结果表明,与传统的融合算法比较在客观性能指标上得到提高。

关 键 词:多目标优化  多目标粒子群优化  图像融合

Image Fusion based on an improved algorithm of Multi-objective Particle swarm Optimization
LI Juan,NAN Xu-liang,BI Si-yuan,WU Wei.Image Fusion based on an improved algorithm of Multi-objective Particle swarm Optimization[J].Journal of Jilin University:Eng and Technol Ed,2013(Z1):477-480.
Authors:LI Juan  NAN Xu-liang  BI Si-yuan  WU Wei
Affiliation:1(1.College of Communication Engineering,Jilin University,Changchun 130022,China;2.Department of Electronic Engineering,Tsinghua University,Beijing 100083,China)
Abstract:Through studying and simulating the traditional algorithm of image fusion and Inspiring by the multi-objective particle swarm optimization,we proposing an improved algorithm of MOPSO.The new algorithm based on multi-objective particle swarm algorithm framework.However,there are some differences between them.The new algorithm adopts more effective ways of speed changing and multi-objective choice processing which makes better performance and the searching solutions closing to the Pareto optimal solution set.The new algorithm has been used for remote sensing images fusion and multi-focus images fusion,which have achieved better results.
Keywords:multi-objective optimization  MOPSO  image fusion
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