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Overview: The infrared intensity image mainly reflects the shape, brightness, and position information of the target. The infrared polarization image mainly reflects the edge and details of the target. The fusion of the two can describe the target information more comprehensively, and plays an important role in the fields of space exploration, target identification, and security detection. Due to the increasingly complex scene information, the traditional fusion algorithm cannot meet the fusion requirements of the difference features between the two types of images, and cannot dynamically adjust the fusion method, resulting in poor fusion or even failure. Therefore, how to dynamically adjust the fusion algorithm based on different image difference features is necessary for dual-modality infrared image fusion. For the improvement of the fusion algorithm, researchers have done a series of studies, but in most cases either the fusion rules are improved or the fusion parameters are optimized, or a combination of multiple algorithms or changes in the fusion structure are performed for a single part. Those improvement cannot make active adjustments based on changes in the difference characteristics. The current dual-mode infrared image lacks the selection and combination basis of each element when constructing the fusion method, and the fusion model cannot dynamically adjust for the image difference feature, resulting in poor fusion effect. Aiming at the above problems, referring to the multi-parameters of biological characters, this paper proposes infrared intensity and polarization image mimicry fusion based on the combination of variable elements and matrix theory. Firstly, the fusion model was divided into four parts: fusion algorithm, fusion rule, fusion parameter and fusion structure. The single mapping relationship between different parts and the difference of image feature fusion is established. Secondly, using the imaginary transformation idea, the imaginary transformation fusion method was established, and the necessary four parts of the fusion process are combined to derive a new fusion algorithm. Finally, it used the different source images with different features to verify the proposed mimetic fusion algorithm. Experimental results show that when the image difference features are different, the fusion method was more suitable for deriving image features, so as to achieve the active selection and adjustment of the fusion algorithm. The fusion method dynamically adjusts the selection of each element in the fusion model using the idea of mimicry transformation, and provides a theoretical basis for its combination, so as to obtain an optimal fusion method. The proposed method gives full play to the advantages of each part and can significantly improve the fusion quality of image difference features, so that the main differences in the source images are well integrated.
Comparison of fusion algorithms for different image features. (a) The fusion degree of fusion algorithm for edge feature; (b) The fusion degree of fusion algorithm for texture features; (c) The fusion degree of fusion algorithm for brightness feature
Comparison of four low - frequency fusion rules for different image features. (a) Fusion of high frequency fusion rules for edge feature; (b) High frequency fusion rules for texture feature fusion; (c) High frequency fusion rules for brightness feature fusion degree
Comparison of four kinds of high frequency fusion rules for different image features. (a) Low frequency fusion rule for edge feature fusion degree; (b) Low frequency fusion rules for texture feature fusion; (c) Low frequency fusion rule for brightness feature fusion degree
Fusion algorithm, (low frequency, high frequency) fusion rule effective fusion mapping. (a) The fusion algorithm is effective for the image edge feature; (b) The low frequency fusion rule is an effective fusion map for image features; (c) The high frequency fusion rule is an effective fusion map for image features
Experimental results of fusion structure validity. (a1) Infrared light intensity; (b1) Infrared polarization; (c1) Serial; (d1) Paralle; (e1) Embedded; (a2) Infrared light intensity; (b2) Infrared polarization; (c2) Serial; (d2) Parallel; (e2) Embedded
Optimized multi-set value mapping
The algorithm flow chart
The original images used in the experiment
Experimental results