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闫纲琦, 梁宗林, 宋延嵩, 董科研, 张博, 刘天赐, 张雷, 王岩柏. 二八杠棋牌游戏网页版[J]. 中国二八杠棋牌游戏(中英文). doi: 10.37188/CO.2023-0196
引用本文: 闫纲琦, 梁宗林, 宋延嵩, 董科研, 张博, 刘天赐, 张雷, 王岩柏. 二八杠棋牌游戏网页版[J]. 中国二八杠棋牌游戏(中英文). doi: 10.37188/CO.2023-0196
YAN Gang-qi, LIANG Zong-lin, SONG Yan-song, DONG Ke-yan, ZHANG Bo, LIU Tian-ci, ZHANG LEI, WANG Yan-bo. 二八杠棋牌游戏官方入口[J]. Chinese Optics. doi: 10.37188/CO.2023-0196
Citation: YAN Gang-qi, LIANG Zong-lin, SONG Yan-song, DONG Ke-yan, ZHANG Bo, LIU Tian-ci, ZHANG LEI, WANG Yan-bo. 二八杠棋牌游戏官方入口[J]. Chinese Optics. doi: 10.37188/CO.2023-0196

二八杠棋牌游戏网页版

doi: 10.37188/CO.2023-0196
基金项目:国家重点研发计划(No. 2022YFB3902500);国家重点研发计划项目(No. 2021YFA0718804);国家自然科学基金青年基金(No. 62305032)
详细信息
    作者简介:

    闫纲琦(1996—),男,河北张家口人,长春理工大学硕士研究生,主要从事图像处理方面的研究。E-mail:[email protected]

    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:[email protected]

  • 中图分类号:TP394.1;TH691.9

二八杠棋牌游戏官方入口

Funds:Supported by National key research and development program (No. 2022YFB3902500); National key research and development program (No. 2021YFA0718804); National Science Foundation of China (No. 62305032)
More Information
  • 摘要:

    随着光谱成像技术的飞速发展,使用多光谱滤光片阵列(Multispectral filter array,MSFA)采集多光谱图像的空间和光谱信息已经成为研究热点。如何利用低采样率且强频谱互相关性的原始数据进行重构成为了瓶颈和制约。因此基于一种含有全通波段的8波段4×4MSFA,提出了空谱联合的多分支注意力残差网络模型。首先使用多分支模型对各个波段插值后的图像特征进行学习。其次八个波段和全通波段的特征信息联合通过本文设计的空间通道注意力模型,应用多层卷积和卷积注意力模块并使用残差补偿的方式可以有效的弥补各波段的颜色差异和丰富边缘纹理相关特征信息。最后初步插值的全通波段和其余波段特征信息通过无需进行批量归一化的残差密集块对多光谱图像空间和光谱相关性进行特征学习以匹配各个波段的光谱信息。实验结果表明,本文对于在D65光源下测试图像的峰值信噪比、结构相似度和光谱角相似度分别优于最先进深度学习方法3.46%、0.27%和6%,并且此方法不仅减少了伪影还获得更多的纹理细节。

  • 图 1 配备MSFA的单传感器多光谱相机的原始图像采集与重构过程

    Figure 1. Original image acquisition and reconstruction process of a single sensor multispectral camera equipped with MSFA

    图 2 多分支注意力残差网络模型框架

    Figure 2. A multi-branch attention residual network model framework

    图 3 马赛克通道卷积块结构图

    Figure 3. Mosaic channel convolutional block structure diagram

    图 4 空间通道注意模型结构图

    Figure 4. Structural diagram of the spatial channel attention model

    图 5 D65光源下的测试图像在sRGB颜色空间中的去马赛克效果视觉对比

    Figure 5. Visual comparison of the de-mosaicing effect of test images under the D65 light source in sRGB color space

    图 6 测试图像在679 nm处不同场景的去马赛克误差图视觉比较

    Figure 6. Visual comparison of de-mosaicing error maps of test images in different scenarios at 679 nm

    图 7 测试图像CD不同波段的去马赛克误差图视觉比较

    Figure 7. Visual comparison of de-mosaicing error maps in different bands of test image CD

    表  1 测试图像在A光源下的PSNR、SSIM和SAM值

    Table  1. PSNR, SSIM, and SAM values of the test image under the A light source

    Methods PSNR ↑ SSIM ↑ SAM ↓
    WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours
    balloons 39.99 43.23 45.52 45.93 0.9977 0.9988 0.9992 0.9993 4.892 3.693 3.409 3.329
    beads 28.35 31.03 32.75 33.57 0.9610 0.9768 0.9776 0.9858 10.087 8.002 7.571 6.182
    Egyptian 35.22 40.77 40.96 43.65 0.9908 0.9947 0.9953 0.9972 11.358 10.648 8.653 7.570
    feathers 32.38 36.32 37.03 38.48 0.9908 0.9962 0.9964 0.9976 8.351 6.782 6.036 5.960
    paints 32.39 36.29 35.79 38.63 0.9939 0.9973 0.9973 0.9984 6.557 5.178 4.891 4.500
    pompoms 36.37 38.28 40.54 41.02 0.9953 0.9968 0.9978 0.9987 4.395 3.524 3.003 2.916
    CD 36.54 38.54 39.01 39.75 0.9939 0.9953 0.9956 0.9969 4.211 4.043 3.956 4.145
    Character 29.27 34.91 38.91 39.51 0.9942 0.9981 0.9989 0.9992 6.103 3.811 3.119 3.049
    ChartRes 30.24 31.26 31.45 32.63 0.9931 0.9976 0.9984 0.9996 3.962 2.761 2.529 1.300
    Average 33.41 36.73 37.99 39.24 0.9900 0.9946 0.9951 0.9969 6.657 5.383 4.796 4.327
    下载: 导出CSV

    表  2 测试图像在D65光源下的PSNR、SSIM和SAM值

    Table  2. PSNR, SSIM, and SAM values of the test image under the D65 light source

    Methods PSNR ↑ SSIM ↑ SAM ↓
    WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours
    balloons 40.73 44.16 46.94 48.36 0.9981 0.9991 0.9995 0.9997 3.629 3.114 2.700 2.539
    beads 25.75 31.28 33.21 34.06 0.9662 0.9826 0.9808 0.9898 10.316 8.324 6.957 6.243
    Egyptian 39.29 42.5 42.58 44.86 0.9929 0.9875 0.9965 0.9979 10.642 8.015 7.038 6.919
    feathers 32.67 36.72 37.26 39.12 0.9916 0.9931 0.9966 0.9980 7.377 5.891 5.412 4.843
    paints 31.18 36.07 36.41 38.90 0.9922 0.9974 0.9887 0.9986 6.408 4.788 3.974 3.905
    pompoms 36.88 38.90 41.19 42.65 0.9962 0.9978 0.9985 0.9989 4.032 3.232 2.693 2.753
    CD 34.54 38.21 40.61 41.53 0.9949 0.9952 0.9961 0.9967 3.209 2.942 2.772 2.726
    Character 28.88 34.90 39.63 39.87 0.9936 0.9984 0.9994 0.9996 6.867 4.100 3.108 2.907
    ChartRes 29.34 29.65 32.54 33.21 0.9951 0.9958 0.9969 0.9971 2.840 2.290 1.561 1.324
    Average 33.25 36.93 38.93 40.28 0.9912 0.9941 0.9947 0.9974 6.147 4.744 4.023 3.795
    下载: 导出CSV

    表  3 测试图像在F12光源下的PSNR、SSIM和SAM值

    Table  3. PSNR, SSIM, and SAM values of the test image under the F12 light source

    Methods PSNR ↑ SSIM ↑ SAM ↓
    WB PPID MGCC Ours WB PPID MGCC Ours WB PPID MGCC Ours
    balloons 36.68 37.83 38.60 38.91 0.9937 0.9952 0.9961 0.9989 5.540 5.640 5.537 5.955
    beads 25.84 27.65 27.67 29.92 0.9390 0.9566 0.9673 0.9720 14.958 13.669 13.532 11.424
    Egyptian 36.23 37.46 38.23 39.47 0.9825 0.9857 0.9862 0.9913 6.150 5.740 4.763 3.795
    feathers 30.61 32.76 33.51 34.70 0.9829 0.9887 0.9889 0.9928 11.526 11.194 11.251 10.897
    paints 28.02 30.89 31.98 33.93 0.9761 0.9871 0.9912 0.9939 9.801 9.440 9.415 9.113
    pompoms 32.17 32.81 33.92 33.97 0.9874 0.9889 0.9907 0.9909 6.776 6.355 6.150 5.936
    CD 35.58 35.99 36.26 37.06 0.9869 0.9872 0.9898 0.9988 7.934 6.691 5.957 6.622
    Character 26.11 28.86 32.33 32.64 0.9824 0.9908 0.9954 0.9962 7.794 6.881 6.835 6.767
    ChartRes 25.81 26.32 27.61 29.33 0.9768 0.9814 0.9826 0.9916 3.845 3.157 2.837 1.859
    Average 30.78 32.28 33.34 34.44 0.9786 0.9846 0.9875 0.9918 8.258 7.640 7.364 6.929
    下载: 导出CSV

    表  4 测试图像在三种光源下的PSNR、SSIM和SAM与不同去马赛克方法的定量比较

    Table  4. Quantitative comparison of PSNR, SSIM, and SAM of test images under three light sources and different de-mosaicing methods

    WB PPID MGCC Ours
    PSNR 29.37 34.56 35.69 37.81
    SSIM 0.8926 0.9705 0.9902 0.9936
    SAM 8.541 6.473 5.647 5.312
    下载: 导出CSV

    表  5 不同去马赛克方法运行时间比较(单位:ms)

    Table  5. Comparison of running times of different de-mosaicing methods(Unit:ms)

    WB PPID MGCC Ours
    CPU 254.35 2134.5 - -
    GPU - - 2.65 2.12
    下载: 导出CSV

    表  6 不同网络架构的消融研究

    Table  6. Research on the ablation of different network architectures

    多分支架构 残差密集块 PSNR SSIM SAM
    Ο Ο 36.94 0.9869 7.263
    Ο Π 37.21 0.9907 6.726
    Π Π 38.59 0.9938 6.218
    下载: 导出CSV

    表  7 不同注意力机制方案的消融研究

    Table  7. Ablation study of different attention mechanism schemes

    注意力机制 PSNR SSIM SAM
    RN 36.24 0.9804 8.352
    CA 36.79 0.9873 6.316
    MAM 37.28 0.9927 6.047
    SCAM 38.46 0.9951 5.247
    下载: 导出CSV
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