Compressive Image Fusion Based on Particle Swarm Optimization Algorithm

DOI

10.2991/esac-15.2015.73How to use a DOI?

Keywords

Compressive sensing, Image fusion, Data similarity, Average gradient, Particle swarm optimization

Abstract

In this paper, we propose a novel compressive image fusion method based on multi-objective particle swarm optimization. In compressive image fusion, the challenge is to choose proper fusion parameter, particle swarm optimization who is a based stochastic optimization technique can solve the challenge. In order to get appropriate parameter, the fitness function select the average gradient function, data similarity function, standard deviation function. The experimental results indicate that the proposed method in MI, Qw, Qe, QAB|F four evaluation indexes have better performance, our method can get more information from the source images and retained more structural information and edge information.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xushuai Li
AU  - Lin Ni
PY  - 2015/08
DA  - 2015/08
TI  - Compressive Image Fusion Based on Particle Swarm Optimization Algorithm
BT  - Proceedings of the 2015 International Conference on Electronic Science and Automation Control
PB  - Atlantis Press
SP  - 300
EP  - 303
SN  - 2352-538X
UR  - https://doi.org/10.2991/esac-15.2015.73
DO  - 10.2991/esac-15.2015.73
ID  - Li2015/08
ER  -