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Infrared and visible images fusion based on improved multi-scale structural fusion
Information Sciences | 更新时间：2024-05-06
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    • Infrared and visible images fusion based on improved multi-scale structural fusion

    • “针对弱光环境下红外与可见光图像融合面临的挑战，某研究团队提出了一种创新的多尺度结构化融合方法。这一方法首先利用动态范围压缩算法增强弱光下的可见光图像，然后通过多尺度结构化分解将增强后的图像与红外图像分解为低频和高频信息。在融合过程中，研究团队创新性地采用均方根误差系数融合低频信息，并通过信息熵自适应调整权重策略对高频信息进行二次优化。最后，通过灰度分类的区域像素增强算法提升融合图像的对比度。实验结果显示，该方法在TNO和CVC-14数据集上表现出色，不仅在平均梯度、交叉熵、边缘强度、标准差和空间频率等指标上取得了更好的客观评价结果，而且整体视觉效果也显著优于其他对比方法。这一突破性的研究成果不仅丰富了图像融合技术的细节纹理和清晰度，还显著缩短了融合耗时，为弱光环境下的图像融合提供了新的解决方案。”
      ”
    • Optics and Precision Engineering   Vol. 32, Issue 7, Pages: 1101-1110(2024)

    • DOI：10.37188/OPE.20243207.1101    

      CLC： TP391

    • Published：10 April 2024，

      Received：18 September 2023，

      Revised：15 November 2023，

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  • LONG Zhiliang,DENG Yueming,XIE Jing,et al.Infrared and visible images fusion based on improved multi-scale structural fusion[J].Optics and Precision Engineering,2024,32(07):1101-1110. DOI： 10.37188/OPE.20243207.1101.

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