Image Fusion Algorithm by Improved Chaos Immune Genetic Algorithm in Multi-Wavelet Transform Domain

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.


2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.


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