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Infrared and Visible Image Fusion via Test-Time Training

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14434))

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Abstract

Infrared and visible image fusion (IVIF) is a widely used technique in instrument-related fields. It aims at extracting contrast information from the infrared image and texture details from the visible image and combining these two kinds of information into a single image. Most auto-encoder-based methods train the network on natural images, such as MS-COCO, and test the model on IVIF datasets. This kind of method suffers from domain shift issues and cannot generalize well in real-world scenarios. To this end, we propose a self-supervised test-time training (TTT) approach to facilitate learning a better fusion result. Specifically, a new self-supervised loss is developed to evaluate the quality of the fusion result. This loss function directs the network to improve the fusion quality by optimizing model parameters with a small number of iterations in the test time. Besides, instead of manually designing fusion strategies, we leverage a fusion adapter to automatically learn fusion rules. Experimental comparisons on two public IVIF datasets validate that the proposed method outperforms existing methods subjectively and objectively.

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Acknowledgements

The work was supported in part by the National Natural Science Foundation of China under Grant 82172033, U19B2031, 61971369, 52105126, 82272071, 62271430, and the Fundamental Research Funds for the Central Universities 20720230104.

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Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, China

    Guoqing Zheng, Zhenqi Fu, Xiaopeng Lin, Xueye Chu, Yue Huang & Xinghao Ding

  2. Institute of Artificial Intelligence, Xiamen University, Xiamen, China

    Yue Huang & Xinghao Ding

Authors
  1. Guoqing Zheng
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  2. Zhenqi Fu
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  3. Xiaopeng Lin
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  4. Xueye Chu
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  5. Yue Huang
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  6. Xinghao Ding
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Corresponding author

Correspondence to Xinghao Ding .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zheng, G., Fu, Z., Lin, X., Chu, X., Huang, Y., Ding, X. (2024). Infrared and Visible Image Fusion via Test-Time Training. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14434. Springer, Singapore. https://doi.org/10.1007/978-981-99-8549-4_7

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  • DOI: https://doi.org/10.1007/978-981-99-8549-4_7

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  • Online ISBN: 978-981-99-8549-4

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