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It is always a goal to fully exploit difference features and their importance in infrared polarization and intensity images to drive selection of fusion algorithms rather than using fixed algorithm, to improve pertinence and effectiveness of image fusion. However, it is difficult to obtain a better and more reasonable result for fused image due to the varied difference features and uncertain relationship between difference features and fusion algorithms. The study aims to investigate the two-tuple linguistic cloud model for possibility distribution to solve the fusion algorithm selection problem. Firstly, fusion validity distributions of difference feature amplitudes are constructed through giving consideration to both fuzziness and randomness. Secondly, in order to build set-valued mapping between difference feature amplitudes and fusion algorithms, a novel fusion validity degree transformation method into two-tuple linguistic cloud variable is proposed based on possibility theory. Thirdly, difference feature weights are calculated based on nonparametric estimation for the frequency of each corresponding feature in images. Next, a cloud weighted arithmetic averaging operator is constructed to rank the algorithms. Finally, a case of the fusion algorithm selection is provided to illustrate the implementation process and applicability of the method proposed in this paper. In addition, the proposed method can be utilized the comprehensive and multi-constrained optimal problem with clear and effective management process.

Set-Valued Mapping Cloud Model and its Application for Fusion Algorithm Selection of Dual Mode Infrared Images