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基于改进引导滤波器的多光谱去马赛克方法

齐海超 宋延嵩 张博 梁宗林 闫纲琦 薛佳音 张轶群 任斌

齐海超, 宋延嵩, 张博, 梁宗林, 闫纲琦, 薛佳音, 张轶群, 任斌. 基于改进引导滤波器的多光谱去马赛克方法[J]. 中国光学(中英文), 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231
引用本文: 齐海超, 宋延嵩, 张博, 梁宗林, 闫纲琦, 薛佳音, 张轶群, 任斌. 基于改进引导滤波器的多光谱去马赛克方法[J]. 中国光学(中英文), 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231
QI Hai-chao, SONG Yan-song, ZHANG Bo, LIANG Zong-lin, YAN Gang-qi, XUE Jia-yin, ZHANG Yi-qun, REN Bin. Multispectral demosaicing method based on an improved guided filter[J]. Chinese Optics, 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231
Citation: QI Hai-chao, SONG Yan-song, ZHANG Bo, LIANG Zong-lin, YAN Gang-qi, XUE Jia-yin, ZHANG Yi-qun, REN Bin. Multispectral demosaicing method based on an improved guided filter[J]. Chinese Optics, 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231

基于改进引导滤波器的多光谱去马赛克方法

doi: 10.37188/CO.2022-0231
基金项目: 国家重点研发计划资助项目(No. 2022YFB3902500);国家自然科学基金资助项目(No. U2141231);吉林省自然科学基金(No. 202002036JC);鹏城实验室重大攻关项目(No. PCL2021A03-1)
详细信息
    作者简介:

    齐海超(1997—),男,吉林长春人,硕士研究生,2019年于长春理工大学获得学士学位,主要从事图像处理方面的研究。E-mail:qihaichao2019@163.com

    宋延嵩(1983—),男,吉林长春人,博士,研究员,博士生导师,2006年、2009年、2014年于长春理工大学分别获得学士、硕士及博士学位,主要研究方向为空间激光通信技术。E-mail: songyansong2006@126.com

  • 中图分类号: TP391.9

Multispectral demosaicing method based on an improved guided filter

Funds: Supported by National Key R & D Program of China (No. 2022YFB3902500); National Natural Science Foundation of China (No.U2141231); the Natural Science Foundation of Jilin Province (No. 202002036JC); The Major Key Project of PCL (No. PCL2021A03-1)
More Information
  • 摘要:

    为了更好地保留多光谱去马赛克图像中的高频信息,本文提出了一种基于改进引导滤波器的多光谱图像去马赛克方法。首先,基于自回归模型对相邻像素点间的强相关性进行建模,在每个像素处渐进估计其模型参数,通过最小化局部窗口内的估计误差,得到最优估计值来插值采样密集波段G,并生成高质量的引导图像;然后,引入加窗固有变分系数到惩罚因子中,得到具有边缘感知能力的加权引导滤波器并重建其余稀疏采样波段。最后,使用CAVE数据集和TokyoTech数据集进行仿真。实验结果表明:相较于主流的5波段多光谱图像去马赛克方法,本方法重建图像的峰值信噪比和结构相似度在CAVE数据集和TokyoTech数据集上分别提高了3.40%,2.02%,1.34%,0.30%和6.11%,5.95%,2.28%,1.42%,且更好地保留了原始图像的局部结构和颜色信息,减少了边缘伪影和噪声现象的出现。

     

  • 图 1  5波段MSFA(a)二叉树分裂过程及(b)排列模式

    Figure 1.  (a) Binary tree splitting process and (b) arrangement of Five-band MSFA

    图 2  邻域T内像素排列

    Figure 2.  Pixel arrangement in neighborhood T

    图 3  水平-垂直方向上的自回归模型

    Figure 3.  Autoregressive model in the horizontal-vertical direction

    图 4  模型参数估计

    Figure 4.  Estimation of model parameter

    图 5  基于加权引导滤波的去马赛克流程

    Figure 5.  Demosaicing process based on weight-guided filtering

    图 6  不同算法Balloons场景重建图像对比

    Figure 6.  Comparison of Balloons images reconstructed by different algorithms

    图 7  不同算法CD场景重建图像对比

    Figure 7.  Comparison of CD images reconstructed by different algorithms

    图 8  不同算法Party场景重建图像对比

    Figure 8.  Comparison of party images reconstructed by different algorithms

    表  1  CAVE数据集上3种方法的客观评价指标

    Table  1.   Objective evaluation metrics of the three methods on the CAVE dataset

    CAVEsRGB PSNR/dBsRGB SSIMCIEDE 2000
    GFAPMIDProGFAPMIDProGFAPMIDPro
    Balloons41.6242.6843.110.98590.99160.99361.181.060.99
    Clay37.3337.6338.690.87580.88170.88521.070.940.89
    Beers41.5742.1843.520.98160.98680.98941.251.281.07
    Lemons42.9142.8742.910.97490.98050.98221.111.081.03
    Peppers42.1442.0842.520.97150.98010.98050.890.780.73
    Feathers35.6435.9435.990.94130.95930.96142.302.102.04
    Flowers38.9341.1842.500.95230.97780.98241.260.910.83
    Paints36.1634.8836.320.96960.97290.97972.402.432.17
    Apples45.2445.1045.680.98380.98750.98860.770.750.70
    Toys38.8341.2942.770.96660.98450.98911.240.900.80
    Avg40.0440.5841.400.96030.97030.97321.351.221.13
    下载: 导出CSV

    表  2  TokyoTech数据集上3种方法的客观评价指标

    Table  2.   Objective evaluation metrics of the three methods on the TokyoTech dataset

    TokyoTechsRGB PSNR/dBsRGB SSIMCIEDE 2000
    GFAPMIDProGFAPMIDProGFAPMIDPro
    Butterfly37.5338.9540.440.95960.96780.98101.601.421.17
    Butterfly338.4242.9441.990.94870.97770.97931.370.910.84
    Butterfly440.6340.7342.370.96910.95900.98271.121.230.87
    CD32.2032.7832.870.94500.95800.96291.971.721.65
    Character37.7437.4538.140.96730.97360.98351.831.941.73
    Cloth34.1835.0035.870.93210.94810.95733.343.222.75
    Color39.2238.6241.360.97820.96300.98951.742.001.47
    Colorchart42.8344.7947.800.98190.98470.99410.920.770.55
    Fan232.6833.2934.090.92570.94260.96292.632.342.04
    Party32.7933.4535.780.93660.95090.96932.061.661.36
    Avg36.8237.8039.070.95440.96250.97621.861.721.44
    下载: 导出CSV

    表  3  不同方法在两种数据集上的运行时间

    Table  3.   Running times of different methods on the two datasets (s)

    数据集GFAPMIDPro
    CAVE1.490.711.33
    TokyoTech1.360.561.29
    下载: 导出CSV
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  • 收稿日期:  2022-11-13
  • 修回日期:  2022-12-12
  • 网络出版日期:  2023-04-17

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