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Fusion of near-infrared and visible images based on saliency-map-guided multi-scale transformation decomposition

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Abstract

In this research, we propose a near-infrared (NIR) and visible (VIS) image fusion method based on saliency-map-guided multi-scale transform decomposition (SMG-MST) to solve the problem of color distortion. Although the existing NIR and VIS image fusion methods can enhance the texture information of the fused image, they cannot control the scattering of light from objects in the fused image resulting in color distortion. The color distortion region usually has good saliency, so using saliency map to solve the above problem is a good choice. In this paper, a visible image guided by saliency map is introduced in the low frequency part, which can weaken the scattering of too much light from objects in the image. In addition, the local entropy of the NIR is used to guide the visible photon images, so the results contain more details. Both qualitative and quantitative experiments demonstrate the effectiveness of our algorithm, and the comparison of algorithm running times shows the high efficiency of our method.

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Data Availability

All public images in the manuscript can be found at:https://www.epfl.ch/labs/ivrl/research/downloads/rgb-nir-scene-dataset/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China nos. 62073304, 41977242 and 61973283.

Author information

Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Chen Jun, Cai Lei & Liu Wei

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, China

    Chen Jun, Cai Lei & Liu Wei

  3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, China

    Chen Jun, Cai Lei & Liu Wei

  4. Chinese Academy of Sciences, Shanghai Institute of Technical Physics, Shanghai, 200083, China

    Yu Yang

  5. Key Laboratory of Infrared System Detecting and Imaging Technology, Chinese Academy of Sciences, Shanghai, 200083, China

    Yu Yang

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  1. Chen Jun

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  2. Cai Lei

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  3. Liu Wei

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  4. Yu Yang

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Corresponding author

Correspondence toChen Jun.

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Jun, C., Lei, C., Wei, L.et al. Fusion of near-infrared and visible images based on saliency-map-guided multi-scale transformation decomposition.Multimed Tools Appl82, 34631–34651 (2023). https://doi.org/10.1007/s11042-023-14709-2

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