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Volume 51 Issue 7
Jul.  2025
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Journal of Beijing University of Aeronautics and Astronautics  > 2025  >  51(7): 2196-2208.
YANG Y，LIU J X，HUANG S Y，et al. Fuzzy logic and adaptive strategy for infrared and visible light image fusion[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2196-2208 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0383
Citation: YANG Y，LIU J X，HUANG S Y，et al. Fuzzy logic and adaptive strategy for infrared and visible light image fusion[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2196-2208 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0383
shu

Fuzzy logic and adaptive strategy for infrared and visible light image fusion

doi: 10.13700/j.bh.1001-5965.2023.0383
  • 1.

    School of Computer Science and Technology，Tiangong University，Tianjin 300387，China

  • 2.

    Cangzhou Institute of Tiangong University，Cangzhou 061000，China

  • 3.

    School of Information Management and Mathematics，Jiangxi University of Finance and Economics，Nanchang 330032，China

  • 4.

    School of Software，Tiangong University，Tianjin 300387，China

  • 5.

    School of Control Science and Engineering，Tiangong University，Tianjin 300387，China

Funds:

National Natural Science Foundation of China (62072218,61862030); Natural Science Foundation of Tianjin (24JCZDJC00130); Project of Cangzhou Institute of Tiangong University (TGCYY-Z-0303)

More Information
  • Corresponding author: E-mail：shuyinghuang2010@126.com
  • Received Date: 16 Jun 2023
  • Accepted Date: 24 Nov 2023
    • Available Online: 17 Apr 2025
  • Publish Date: 14 Apr 2025
    Abstract

  • Due to different imaging mechanisms, infrared imaging can capture target information under special conditions where the targetis obstructed, while visible light imaging can capture the texture details of the scenarios. Therefore, to obtain a fusion image containing both target information and texture details, infrared imaging and visible light imaging are generally combined to facilitate visual perception and machine recognition. Based on fuzzy logic theory, an infrared and visible light image fusion method was proposed,combining multistage fuzzy discrimination and adaptive parameter fusion strategy (MFD-APFS). First, the infrared and visible light images were decomposed into structural patches, obtaining the contrast-detail image set reconstructed by the signal intensity component. Second, the source image stand contrast-detail image set were processed through a designed fuzzy discrimination system, generating saliency maps for each set. A second-stage fuzzy discrimination was then applied to produce a unified saliency map. Finally, the guided filtering technique was used, with the saliency map guiding the source image to obtain multiple decision graphs. The final fusion image was obtained through the adaptive parameter fusion strategy. The proposed MFD-APFS method was experimentally evaluated on publicly available infrared and visible light datasets. Compared to the seven mainstream fusion methods, the proposed method shows improvements in objective metrics. On the TNO dataset, SSIM-F and QAB/F were improved by 0.169 and 0.1403, respectively, and on the RoadScenes dataset, they were improved by 0.1753 and 0.0537, respectively. Furthermore, the subjective visual analysis indicates that the proposed method is capable of generating fusion images with clear targets and enriched details while retaining infrared target information and visible texture information.

     

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