吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 477-480.

Previous Articles     Next Articles

Image Fusion based on an improved algorithm of Multi-objective Particle swarm Optimization

LI Juan1, NAN Xu-liang1, BI Si-yuan2, WU Wei1   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130022, China;
    2. Department of Electronic Engineering, Tsinghua University, Beijing 100083, China
  • Received:2012-08-25 Published:2013-06-01

PDF (PC)

530

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.

Key words: multi-objective optimization, MOPSO, image fusion

CLC Number: 

  • TP391

[1] Goshtasby A A, Nikolov S. Image fusion: Advances in the state of the art[J]. Information Fusion, 2007,8(2):114-118.

[2] Burt P J, Kolczynski R J. Enhanced image capture through fusion[C]//Proceedings of the 4th IEEE International Conference on Computer Vision,1993:173-182.

[3] Pajares G, Cruz J. Awavelet-based image fusion tutorial[J]. Pattern Recognition,2004,37(9):1855-1872.

[4] Zhang Qiang, Guo Bao-long. Multifocus image fusion using the nonsubsampled contourlet transform[J]. Signal Processing,2009,89(7):1334-1346.

[5] Kennedy J, Eberhart R C. Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Net works Piscataway, 1995, 4:1942-1948.

[6] Coello C A, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004,8(3):256-279.

[7] Kennedy J. The particle swarm: Social adaptation of knowledge[C]//Proc IEEE Int Conference on Evolutionary Computation. Indianapolis, 1997. 303-308.

[8] Hadjisavvas N, Pardalos P. Advances in convex analysis and global optimization[C]//The Nether lands, Kluwer Academic Publishers, 2001.445-457.
[1] LIU Zhe, XU Tao, SONG Yu-qing, XU Chun-yan. Image fusion technology based on NSCT and robust principal component analysis model with similar information [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1614-1620.
[2] QIU Xiao-ming, WANG Yin-xue, YAO Han-wei, FANG Xue-qing, XING Fei. Multi-objective optimization of resistance spot welding parameters for DP1180/DP590 using grey relational analysis based Taguchi [J]. 吉林大学学报(工学版), 2018, 48(4): 1147-1152.
[3] WANG Deng-feng, ZHANG Shuai, WANG Yong, CHEN Hui. Optimization design of assembled wheel based on performance of fatigue and 13° impact [J]. 吉林大学学报(工学版), 2018, 48(1): 44-56.
[4] YU Fan-hua, LIU Ren-yun, ZHANG Yi-min, ZHANG Xiao-li, SUN Qiu-cheng. Swarm intelligence algorithm of dynamic reliability-based robust optimization design of mechanic components [J]. 吉林大学学报(工学版), 2017, 47(6): 1903-1908.
[5] ZHOU Fang, SONG Chuan-xue, LIANG Tian-wei, XIAO Feng. Parameter matching of on-board hybrid energy storage system using NSGA-II algorithm [J]. 吉林大学学报(工学版), 2017, 47(5): 1336-1343.
[6] LIU Ying, ZHANG Kai, YU Xiang-jun. Multi-objective optimization of hydrostatic bearing of hollow shaft based on surrogate model [J]. 吉林大学学报(工学版), 2017, 47(4): 1130-1137.
[7] HU Kan, YU Ye, YING Liang, HU Ping, HOU Wen-bin. Optimization design of hot-stamping beam structure considering rollover crash safety of school bus [J]. 吉林大学学报(工学版), 2017, 47(3): 884-890.
[8] YIN Ming, ZHAN Yin-wei, PEI Hai-long. Co-sparse analysis operator learning for image fusion [J]. 吉林大学学报(工学版), 2016, 46(6): 2052-2058.
[9] YU Fan-hua, LIU Ren-yun, ZHANG Yi-min, SUN Qiu-cheng, ZHANG Xiao-li. Intelligent algorithm for optimized dynamic reliability design of mechanic structure [J]. 吉林大学学报(工学版), 2016, 46(4): 1269-1275.
[10] ZHANG Dong-hao, XIANG Chang-le, HAN Li-jin, ZHENG Hai-liang. Multi-objective optimization hierarchic control method for power-split hybrid electric vehicles [J]. 吉林大学学报(工学版), 2016, 46(2): 373-382.
[11] ZHU Bing, JIA Xiao-feng, WANG Yu, WU Jian, ZHAO Jian, DENG Wei-wen. Rapid prototype test for integrated vehicle dynamic control using two dSPACE simulators [J]. 吉林大学学报(工学版), 2016, 46(1): 8-14.
[12] ZHOU Jie, LUO Yan, WANG Xun, WANG Hui, LI Yang, TAO Ya-ping. Multi-objective optimization of stamping forming process of head based on response surface model [J]. 吉林大学学报(工学版), 2016, 46(1): 205-212.
[13] ZUO Shu-guang, JIANG Wei-xu, WU Xu-dong, LI Cheng-yi. Vibration transfer characteristics optimization of an electric vehicle's twist beam suspension based on ISIGHT [J]. 吉林大学学报(工学版), 2015, 45(5): 1381-1387.
[14] SONG Kang,CHEN Xiao-kai,LIN Yi. Multi-objective optimization of vehicle ride dynamic behaviors [J]. 吉林大学学报(工学版), 2015, 45(2): 352-357.
[15] DONG Ning,WANG Yu-ping. Guiding multi-objective differential evolution algorithm for constrained optimization [J]. 吉林大学学报(工学版), 2015, 45(2): 569-575.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd