QuickBird Panchromatic and Multi-Spectral Image Fusion Using Wavelet Packet Transform

2006 ◽  
pp. 976-981
Author(s):  
Wenjuan Zhang ◽  
Jiayin Kang
Keyword(s):  
Image Fusion ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Spectral Image

Fusion Method of Infrared and Visible Images Based on Wavelet Packet Transform

2013 ◽  
Vol 333-335 ◽  
pp. 1134-1138
Author(s):  
Hai Tao Su ◽  
Zhan Feng Wang ◽  
Zhi Yi Hu ◽  
Hong Shu Chen ◽  
Jie Liang Wang
Keyword(s):  
Standard Deviation ◽  
Image Fusion ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Wavelet Function ◽  
Decomposition Level ◽  
Feature Information

The multi-sensor image fusion is the effective practices to increase the image information, highlight the detection superiority, reduce fuzzy understanding and to reduce data redundancy. Image fusion based on wavelet transform, the image wavelet decomposition processing only exists in the low-frequency, when the image contains high-frequency information, such as a large number of small edge or texture, which can not extract the feature information of the image, so resulting in the fusion is ineffective. In response to these problems, the use of image fusion algorithm based on wavelet packet transform, continue to break down, while the low-frequency further decomposition of the high-frequency of the image, extracts image feature information more effectively. In the same conditions of wavelet function, decomposition level, the fusion policy, comparative analysis has been researched on wavelet transform and wavelet packet transform on the same parameters of the information entropy, average gradient, standard deviation, spatial frequency, the results show that, image fusion of the algorithm based on wavelet packet transform are the highest and the better. In the other hand, in order to investigate the fusion effectiveness of the decomposition level on the same wavelet function conditions, fusion image parameters, such as entropy, average gradient, standard deviation, and spatial frequency, have been calculated using the db3 wavelet function corresponding to the decomposition level 1-5. The results show that the fusion effectiveness should achieve the best with wavelet decomposition level of 3 or 2.

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