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Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

Girraj Prasad Rathor, Sanjeev Kumar Gupta
Copyright: © 2017 |Pages: 15
ISBN13: 9781522505365|ISBN10: 1522505369|EISBN13: 9781522505372
DOI: 10.4018/978-1-5225-0536-5.ch008
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MLA

Rathor, Girraj Prasad, and Sanjeev Kumar Gupta. "Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform." Pattern and Data Analysis in Healthcare Settings, edited by Vivek Tiwari, et al., IGI Global, 2017, pp. 143-157. https://doi.org/10.4018/978-1-5225-0536-5.ch008

APA

Rathor, G. P. & Gupta, S. K. (2017). Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform. In V. Tiwari, B. Tiwari, R. Thakur, & S. Gupta (Eds.), Pattern and Data Analysis in Healthcare Settings (pp. 143-157). IGI Global. https://doi.org/10.4018/978-1-5225-0536-5.ch008

Chicago

Rathor, Girraj Prasad, and Sanjeev Kumar Gupta. "Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform." In Pattern and Data Analysis in Healthcare Settings, edited by Vivek Tiwari, et al., 143-157. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0536-5.ch008

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Abstract

Image fusion based on different wavelet transform is the most commonly used image fusion method, which fuses the source pictures data in wavelet space as per some fusion rules. But, because of the uncertainties of the source images contributions to the fused image, to design a good fusion rule to incorporate however much data as could reasonably be expected into the fused picture turns into the most vital issue. On the other hand, adaptive fuzzy logic is the ideal approach to determine uncertain issues, yet it has not been utilized as a part of the outline of fusion rule. A new fusion technique based on wavelet transform and adaptive fuzzy logic is introduced in this chapter. After doing wavelet transform to source images, it computes the weight of each source images coefficients through adaptive fuzzy logic and then fuses the coefficients through weighted averaging with the processed weights to acquire a combined picture: Mutual Information, Peak Signal to Noise Ratio, and Mean Square Error as criterion.

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