Infrared intensity and polarization image mimicry fusion based on the combination of variable elements and matrix theory

Lv Sheng; Yang Fengbao; Ji Linna; Wang Xiangdong

doi:10.12086/oee.2018.180188

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Lv Sheng, Yang Fengbao, Ji Linna, et al. Infrared intensity and polarization image mimicry fusion based on the combination of variable elements and matrix theory[J]. Opto-Electronic Engineering, 2018, 45(12): 180188. doi: 10.12086/oee.2018.180188

Citation:

Lv Sheng, Yang Fengbao, Ji Linna, et al. Infrared intensity and polarization image mimicry fusion based on the combination of variable elements and matrix theory[J]. Opto-Electronic Engineering, 2018, 45(12): 180188. doi: 10.12086/oee.2018.180188

Infrared intensity and polarization image mimicry fusion based on the combination of variable elements and matrix theory

1.
School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi 030051, China
2.
Key Laboratory of Electronic Testing Technology, North University of China, Taiyuan, Shanxi 030051, China

Fund Project: Supported by National Natural Science Foundation of China (61672472, 61702465), North University of China Science Research Fund Project (XJJ2016024), and Key Laboratory of Electronic Test Technology of North University of China Open Fund Support Project (ZDSYSJ2015005)

More Information

Corresponding author: Yang Fengbao, E-mail: engbao_yang@163.com

Received Date 13 April 2018

Revised Date 14 May 2018

Published Date 01 December 2018

Abstract

Abstract

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 active selection and adjustment of the fusion algorithm. The different features in the fusion image can be effectively combined, and the visual effect of the original image is significantly improved.
- image fusion /
- infrared imaging /
- matrix theory /
- dynamic reconstruction /
- multi-level mapping

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References

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Overview

Overview

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.