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有限离散剪切波域的红外可见光图像融合

陈清江 张彦博 柴昱洲 魏冰蔗

陈清江, 张彦博, 柴昱洲, 魏冰蔗. 有限离散剪切波域的红外可见光图像融合[J]. 中国光学(中英文), 2016, 9(5): 523-531. doi: 10.3788/CO.20160905.0523
引用本文: 陈清江, 张彦博, 柴昱洲, 魏冰蔗. 有限离散剪切波域的红外可见光图像融合[J]. 中国光学(中英文), 2016, 9(5): 523-531. doi: 10.3788/CO.20160905.0523
CHEN Qing-jiang, ZHANG Yan-bo, CHAI Yu-zhou, WEI Bing-zhe. Fusion of infrared and visible images based on finite discrete shearlet domain[J]. Chinese Optics, 2016, 9(5): 523-531. doi: 10.3788/CO.20160905.0523
Citation: CHEN Qing-jiang, ZHANG Yan-bo, CHAI Yu-zhou, WEI Bing-zhe. Fusion of infrared and visible images based on finite discrete shearlet domain[J]. Chinese Optics, 2016, 9(5): 523-531. doi: 10.3788/CO.20160905.0523

有限离散剪切波域的红外可见光图像融合

doi: 10.3788/CO.20160905.0523
基金项目: 

陕西省自然科学基金资助项目 2015JM1024

陕西省自然科学基金资助项目 2013JK0568

详细信息
    通讯作者:

    陈清江(1966-), 男, 河南信阳人, 博士, 教授, 主要从事小波分析、图像处理与信号处理方面的研究.E-mail:qjchen66xytu@126.com

  • 中图分类号: TP391.4

Fusion of infrared and visible images based on finite discrete shearlet domain

Funds: 

Shaanxi Provincial Natural Science Foundation of China 2015JM1024

Shaanxi Provincial Natural Science Foundation of China 2013JK0568

More Information
  • 摘要: 针对目前图像融合过程中的不足之处,结合有限离散剪切波具有高的方向敏感性和抛物尺度化特性,提出了一种有限离散剪切波变换下的图像融合算法。首先对严格配准的多传感器图像进行有限离散剪切波变换,得到低频子带系数和不同尺度不同方向的高频子带系数;然后对低频子带系数采用全局特征值和像素点之间的差异性与区域空间频率匹配度相结合的融合算法,高频方向子带系数采用方向权重对比度与相对区域平均梯度和相对区域方差相结合的方案;最后通过有限离散剪切波逆变换得到融合图像。实验结果表明,与其他的融合算法相比较,本文算法不但有良好的主观视觉效果,而且3幅图像的客观评价指标分别平均提高了0.9%、3.8%、3.1%,2.6%、3.8%、2.9%和1.5%、125%、59%,充分说明了本文融合算法的优越性。

     

  • 图 1  不同区域的频域平面

    Figure 1.  Frequency domain plane in different regions

    图 2  测试图像及重构图像

    Figure 2.  Test image and reconstructed image

    图 3  第一层和第二层分解的方向子带

    Figure 3.  Directional subband of the first level and second level decomposition

    图 4  不同小波域在文章融合策略下对红外与可见光图的融合结果

    Figure 4.  Infrared and visible image fusion results using fusion strategy of this paper in different wavelet domain

    图 5  不同小波域在不同融合策略下对红外与可见光图(5)的融合结果

    Figure 5.  Infrared and visible image(5) fusion results using different fusion strategies in different wavelet domains

    图 6  不同小波域在不同融合策略下对红外与可见光图(6)的融合结果

    Figure 6.  Infrared and visible image(6) fusion results using different fusion strategies in different wavelet domains

    图 7  不同小波域在不同融合策略下对红外与可见光图(7)的融合结果

    Figure 7.  Infrared and visible image(7) fusion results using different fusion strategies in different wavelet domains

    表  1  不同小波域下对红外与可见光图的融合结果比较

    Table  1.   Fusion results comparison of infrared and visible image in different wavelet domain

    Fusion evaluationInfrared and visible imageAverage running time/s
    ENMIQAB/F
    SWT5.648 11.618 80.389 42.246 5
    DWT5.412 31.521 50.501 82.487 4
    NSCT5.216 51.741 10.391 12.798 1
    DTCWT5.348 91.815 60.384 12.741 6
    NSST5.481 41.815 40.377 53.146 7
    FDST6.841 23.924 80.661 42.194 5
    下载: 导出CSV

    表  2  不同小波域在不同融合策略下对红外与可见光图的融合结果比较

    Table  2.   Fusion results comparison of infrared and visible by using different fusion strategies in different wavelet domain

    Fusion evaluationInfrared and visible image(5)Infrared and visible image(6)Infrared and visible image(7)Averagerunning time/s
    ENMIQAB/FENMIQAB/FENMIQAB/F
    NSST_ HSGLRV6.825 83.038 80.447 97.231 73.152 20.605 37.432 12.439 20.548 810.457 9
    FRWT_HRELV6.732 33.749 10.417 87.132 72.828 20.423 47.287 82.686 10.353 98.407 3
    FDST_LGHRS6.924 72.123 00.474 37.490 12.511 00.646 97.413 03.746 40.686 49.432 7
    FDST_LGVHC7.000 83.870 40.573 27.349 23.868 50.708 07.487 26.460 00.781 76.228 8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-18
  • 修回日期:  2016-05-11
  • 刊出日期:  2016-10-01

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