马氏距离与引导滤波加权的红外与可见光图像融合

刘佳, 李登峰

刘佳, 李登峰. 马氏距离与引导滤波加权的红外与可见光图像融合[J]. 红外技术, 2021, 43(2): 162-169.
引用本文: 刘佳, 李登峰. 马氏距离与引导滤波加权的红外与可见光图像融合[J]. 红外技术, 2021, 43(2): 162-169.
LIU Jia, LI Dengfeng. Infrared and Visible Light Image Fusion Based on Mahalanobis Distance and Guided Filter Weighting[J]. Infrared Technology , 2021, 43(2): 162-169.
Citation: LIU Jia, LI Dengfeng. Infrared and Visible Light Image Fusion Based on Mahalanobis Distance and Guided Filter Weighting[J]. Infrared Technology , 2021, 43(2): 162-169.

马氏距离与引导滤波加权的红外与可见光图像融合

基金项目: 

国家自然科学基金项目“稀疏框架与相关问题研究” 61471410

详细信息
    作者简介:

    刘佳(1997-),女,湖北黄冈人,硕士,主研领域:数字图像处理。E-mail:1769723820@qq.com

    通讯作者:

    李登峰(1964-),男,河南开封人,博士,教授,博士生导师,主研领域:小波分析与图像处理

  • 中图分类号: TP391

Infrared and Visible Light Image Fusion Based on Mahalanobis Distance and Guided Filter Weighting

  • 摘要: 为使红外与可见光融合图像获得更好的分辨率和清晰度,提出基于非下采样轮廓波变换(non-subsampled contourlet transform, NSCT)的马氏距离加权拉普拉斯能量和与引导滤波改进(frequency tuned, FT)结合的红外与可见光图像融合算法。首先,对可见光图像进行对比度受限的自适应直方图均衡(contrast limited adaptive histogram equalization, CLAHE),并将红外图像与CLAHE处理后可见光图像进行NSCT变换,分解为低频和高频; 其次,对FT算法使用引导滤波进行改进,利用改进的FT算法提取红外图像显著性图自适应加权融合低频图像,对高频图像使用基于马氏距离加权的拉普拉斯能量和取大融合; 最后,对融合的低频和高频图像进行NSCT逆变换获得融合图像。实验结果表明,该融合方法相较其他传统融合方法,在主观视觉上和客观指标上都有较好的表现。
    Abstract: To improve the definition of fusion images and obtain better target information during the fusion of infrared and visible light images using the characteristics of non-subsampled contourlet transform(NSCT) coefficients, an Manalanobis distance weighted Laplacian energy combined with guided filtering is proposed to improve the frequency tuned (FT) algorithm. First, the visible light image is subjected to contrast limited adaptive histogram equalization(CLAHE), and the infrared image and the CLAHE processed visible light image are decomposed into a low-frequency approximate image and a high-frequency detail image through a multi-scale and multi-directional NSCT transform. Second, the FT algorithm improved by guided filtering isused to extract the significance graph of infrared images, the adaptive weighted fusion rule based on the significance graph of infrared images is used for low-frequency images, and the fusion rule based on the Laplace energy and maximum weighted by the Manalanobis distance is used for high-frequency images. Finally, the fusion image is obtained by the NSCT inverse transformation of the fused low-frequency and high-frequency images. The experimental results show that this fusion method has better performance in terms of subjective vision and objective indexes than other traditional fusion methods.
  • 图  1   NSCT分解过程

    Figure  1.   The decomposition process of NSCT

    图  2   可见光图像对比度增强

    Figure  2.   Visible image contrast enhancement

    图  3   融合框架

    Figure  3.   Fusion framework

    图  4   可见光源图像与红外源图像

    Figure  4.   Source image of visible light image and infrared image

    图  5   实验图像对比

    Figure  5.   The comparison of experimental images

    表  1   融合图像客观评价结果

    Table  1   Objective evaluation results of fusion image

    Image name Fusion method EI SD AG SF
    Ship   DWT 4.9016 10.4666 1.4100 3.1531
      NSCT 4.9139 10.4807 1.3980 3.1546
      NSCT-FT 5.9540 21.1184 1.6376 3.9024
      NSCT-M 6.5735 25.8154 4.7976 10.1821
    Man   DWT 6.5266 31.5238 2.9829 5.5125
      NSCT 6.5491 31.7851 3.2272 6.3206
      NSCT-FT 7.1864 61.6516 3.4935 7.1168
      NSCT-M 7.6698 58.7864 8.8359 15.5185
    Street   DWT 5.9299 20.6524 3.1668 7.7725
      NSCT 5.9442 21.8888 3.7054 12.7396
      NSCT-FT 5.5269 33.4513 4.0396 13.8090
      NSCT-M 6.8136 41.2933 8.4553 20.3821
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-10
  • 修回日期:  2020-03-18
  • 刊出日期:  2021-02-19

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