Block-based Compressive Sensing Image Fusion Method Based on Particle Swarm Optimization Algorithm

DOI

10.2991/icmmcce-17.2017.141How to use a DOI?

Keywords

Compressive sensing; Particle swarm optimization; Fusion coefficient; self-adaptability

Abstract

This In order to solve the problem that the spatial matching is difficult and the spectral distortion is large in traditional pixel-level image fusion algorithm. In this paper, we proposed an block-based compressive sensing image fusion method based on particle swarm optimization algorithm. We get the compressive measurements of input images by block-based compressive sensing (BCS) and fused them with the rule of linear weighting, while the fusion coefficients ( 1, 2, 3..., n , n is the divided number of blocks of the image to be fused) of each block were selected by particle swarm optimization algorithm. In the iterative process, the image fusion coefficient i is taken as particle, and the optimal value is obtained by combining the optimal objective function, taking the coefficient i as the weight value. The algorithm ensures the optimal selection of fusion effect with a certain degree of self-adaptability. To evaluate the fused images, this paper uses five kinds of index parameters such as Entropy, Standard Deviation, Average Gradient, Degree of Distortion and Peak Signal-to-Noise Ratio. The experimental results show that the image fusion effect of the algorithm in this paper is better than that of traditional methods.

Copyright

© 2017, 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  - Xianhu Li
AU  - Jingguo Lv
AU  - Shan Jiang
AU  - Xin Pan
PY  - 2017/09
DA  - 2017/09
TI  - Block-based Compressive Sensing Image Fusion Method Based on Particle Swarm Optimization Algorithm
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 783
EP  - 786
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.141
DO  - 10.2991/icmmcce-17.2017.141
ID  - Li2017/09
ER  -