Zhang Yaling, Ji Linna, Yang Fengbao, et al. Characterization of dual-mode infrared images fusion based on cosine similarity[J]. Opto-Electronic Engineering, 2019, 46(10): 190059. doi: 10.12086/oee.2019.190059
Citation: Zhang Yaling, Ji Linna, Yang Fengbao, et al. Characterization of dual-mode infrared images fusion based on cosine similarity[J]. Opto-Electronic Engineering, 2019, 46(10): 190059. doi: 10.12086/oee.2019.190059

Characterization of dual-mode infrared images fusion based on cosine similarity

    Fund Project: Supported by National Natural Science Foundation of China (61672472), Science for Youth Fund (61702465), and North University of China Graduate Science and Technology Project (20181530)
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  • To solve the problem of low stability of fusion validity measurement in existing fusion of infrared intensity and polarization images, the stability of various fusion algorithms for the distribution of fusion validity of different features was analyzed and compared by using three common fusion validity function measurement methods based on measurement distance. By calculating the frequency of the optimal fusion algorithm in difference feature amplitude interval of ten groups of images, the optimal fusion algorithms for each difference feature were obtained, and cosine similarity was obtained as a measure of fusion efficiency with high stability and more consistent with subjective observation results of the humans. The experimental results show that the cosine similarity has high stability and good matching with human vision analysis in the fusion effectiveness measurement of various fusion algorithms.
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  • Overview: In the existing fusion of infrared intensity and polarization images, the optimal fusion efficiency measurement method is not sought, and leads to the inability to accurately reflect the real fusion situation in different imaging scenes. Therefore, to solve the above problems, this paper firstly constructs the class sets of difference features and the class sets of fusion algorithms aiming at the image features and fusion features of the dual-mode images. Then, the difference features were defined and the meaning of fusion validity was defined. The fusion validity evaluation functions were constructed by using the distance measurement formulas. Among them, the three common functional representations of distance measurement were Euclidean distance, cosine similarity and Lance and Williams distance. Based on the difference features amplitudes of the maximum and the minimum in the source image, all the difference features amplitudes will be interval equal (here are divided into 20 groups), and the interval of each amplitude will be measured, and gets each amplitude range in the difference features of the approximate fusion validity, and finally gets the source images in 20 amplitude ranges of approximate fusion validity distribution curves, it is concluded that the different variety of fusion algorithms for different features of fusion validity distribution curves. According to the thought that difference features drive selecting the optimal fusion algorithm, the dual-mode images for difference features classes focus on different features of amplitude, through the use of three kinds of measurement for fusion validity based on the concentration of 12 kinds of fusion algorithm, and get fusion validity of discrete points distribution, then the amplitude of difference features intervals was classified. The amplitudes of difference features intervals discrete points are averaged which contributes the curves distribution of fusion validity under different fusion algorithms for each differential feature amplitude. Again in each amplitude range, the algorithm with the maximum fusion validity value is selected. The optimal fusion algorithm in each difference feature amplitude interval and the overall fusion efficiency of the interval represented by the optimal fusion algorithm are also obtained. The frequency of the optimal fusion algorithm in the difference feature amplitude interval of the ten groups of source images was counted, thus the optimal fusion algorithm of each difference feature is obtained. The experimental results show that the cosine similarity has high stability and good matching with human vision analysis in the fusion measurement validity of various fusion algorithms.

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