27 February 2018 Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy
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Abstract
Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.
© 2018 SPIE and IS&T
Lei Zhang, Lei Zhang, Fengbao Yang, Fengbao Yang, Linna Ji, Linna Ji, Sheng Lv, Sheng Lv, } "Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy," Journal of Electronic Imaging 27(1), 013029 (27 February 2018). https://doi.org/10.1117/1.JEI.27.1.013029 . Submission: Received: 25 August 2017; Accepted: 29 January 2018
Received: 25 August 2017; Accepted: 29 January 2018; Published: 27 February 2018
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