Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12903
Title: Multisource image fusion method using support value transform
Authors: Zheng, S
Shi, WZ 
Liu, J
Zhu, GX
Tian, JW
Keywords: Image fusion
Image sensors
Least squares approximations
Support vector machines
Imaging sensors
Least squares SVM
Multisource image fusion
Salient feature
Support value transform
Undecimated transform
Discrete wavelet transforms
Filling
Laplace equations
Least squares methods
Matched filters
Mutual information
Sensor fusion
Mapped least squares support vector machine (mapped LS-SVM)
Support value transform (SVT)
Support vector machine (SVM)
Algorithms
Artificial Intelligence
Image Enhancement
Image Interpretation
Computer-Assisted
Imaging
Three-Dimensional
Pattern Recognition
Automated
Subtraction Technique
Issue Date: 2007
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2007, v. 16, no. 7, p. 1831-1839 How to cite?
Journal: IEEE transactions on image processing 
Abstract: With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (QAB/F) , the mutual information, etc.
URI: http://hdl.handle.net/10397/12903
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2007.896687
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