A fusion algorithm selection method for infrared image based on quality synthesis of intuition possible sets.

• This paper proposes a fusion algorithm selection method for infrared image based on quality synthesis of intuition possible sets. • We develop an effective model to build mapping relationship between difference feature amplitudes of images. • Non-dominated subsets under each fusion algorithm are determined with negative entropy and credibility. • The score function of multiple fusion algorithms is constructed to select optimal fusion algorithm. Aiming at the heterogeneous features are often collaboratively optimized for fusion, and the existing feature attributes cannot be targeted to adjust algorithms to drive fusion effectively, resulting in poor fusion. We put forward infrared image fusion algorithm selection based on quality synthesis of intuition possible sets. Firstly, the fusion validity of difference feature of image is calculated. In view of intuition possible set orderings of multiple intervals of each difference feature, the degrees can be divided into three levels, and the corresponding possibility distribution subsets are obtained. Secondly, we introduce credibility and separability to weight the multiple subsets, and calculate negative entropy and credibility of each synthesized subset, in order to determine non-dominated subsets under each fusion algorithm. Thirdly, the score function of multiple fusion algorithms is constructed to select optimal fusion algorithm. Then we employ thirteen evaluation indicators and their comprehensive score to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]