Multi-resolution Image Fusion Algorithm Based on Gradient and Texture Features

Author(s):  
Junyong Ma ◽  
Shengwei Zhang ◽  
Caibing Yue
2014 ◽  
Vol 989-994 ◽  
pp. 2499-2502
Author(s):  
Jian Cao ◽  
Ti Fang Li ◽  
Yu Peng Gao

In order to make full use of texture features of image when fusing images, and taking into account the inherent advantages of fractal theory in this respect, a novel image fusion algorithm, which used fractal dimension and directional contrast, based on multi-wavelet transform was proposed in this paper. So, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. Moreover, the objective indexes, which are image entropy, standard deviation and quality measure, were adopted to evaluate the comparative results of evaluating fusion quality. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.


2014 ◽  
Vol 602-605 ◽  
pp. 3396-3399
Author(s):  
Jian Cao ◽  
Yun Bai ◽  
Hai Ying Ma

In order to make full use of texture features of image when fusing images, and taking into account the inherent advantages of fractal theory in this respect, a novel image fusion algorithm, which has been applied to large foreign spacecraft already, but late start and only limited to configurations and steering law study in China. Based on the needs of agility small satellites, this article explained the method of SGCMG principle prototype design and proposed the influence on SGCMG output torque precision generated by time accumulation error first time. This article provided a new idea for future SGCMG project development and had good reference value for SGCMG research.


2018 ◽  
Vol 30 (9) ◽  
pp. 1637
Author(s):  
Zhong Xiang ◽  
Jianfeng Zhang ◽  
Miao Qian ◽  
Zhenyu Wu ◽  
Xudong Hu

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