Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 5, 64(2024)

Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation

Xiangyang KONG1, Jiao ZHANG2, Hui WANG1, and Baogen XU3
Author Affiliations
  • 1School of Education, Sichuan Polytechnic University, Deyang 618000, China
  • 2No.1 Gas Production Plant of Southwest Oil and Gas Branch of Sinopec, Deyang 618000, China
  • 3School of Science, East China Jiaotong University, Nanchang 330013, China
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    Figures & Tables(14)
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    • Table 1. MPSNR, MSSIM and MSAM values for random length stripes

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      Table 1. MPSNR, MSSIM and MSAM values for random length stripes

      控制指标评价指标含噪LRMIDDL0RBSRLRTDNLLRTDTV
      I=60r=0.15MPSNR16.3533.2740.6142.3541.2944.63
      MSSIM0.55330.88280.92640.98120.97720.9906
      MSAM0.59710.41220.20250.19360.19750.1129
      MPSNR9.1128.2437.6239.1938.6841.95
      r=0.65MSSIM0.34010.86640.89530.95430.95890.9788
      MSAM0.73580.45630.22350.20870.21160.1421
      MPSNR7.5627.3235.6437.8836.0338.69
      r=0.95MSSIM0.13030.75680.84590.91080.85720.9665
      MSAM0.87160.48680.24810.21970.23860.1608
      I=90r=0.15MPSNR12.8332.6539.3741.5840.3943.27
      MSSIM0.40970.87110.91640.97750.96820.9889
      MSAM0.62960.47180.24610.22390.23640.1558
      r=0.65MPSNR5.5926.3132.5436.8533.1339.52
      MSSIM0.14410.83670.87550.92480.91690.9582
      MSAM0.80180.47760.24510.21680.23180.1582
      r=0.95MPSNR4.0423.5529.1731.7630.1834.69
      MSSIM0.08520.73380.80750.89130.83670.9384
      MSAM0.91770.50130.26390.22670.25740.1772
    • Table 2. MPSNR, MSSIM and MSAM values in the case of global stripes

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      Table 2. MPSNR, MSSIM and MSAM values in the case of global stripes

      条带类型控制指标评价指标含噪LRMIDDL0RBSRLRTDNLLRTDTV
      周期MPSNR22.5642.3651.6251.8952.0454.84
      r=0.15MSSIM0.594 90.999 20.999 60.999 30.999 70.999 9
      MSAM0.091 30.020 50.021 30.022 10.015 80.011 6
      MPSNR14.7841.0248.9750.8551.6253.18
      I=60r=0.65MSSIM0.212 80.979 60.988 40.990 20.994 20.997 9
      MSAM0.319 90.110 30.095 10.096 20.090 40.083 6
      MPSNR13.0238.9149.0249.1143.5453.12
      r=0.95MSSIM0.153 90.867 40.923 80.938 10.906 40.971 5
      MSAM0.517 40.201 50.164 20.153 60.163 80.101 4
      MPSNR19.0442.1351.2151.6651.9554.28
      r=0.15MSSIM0.469 60.998 70.999 10.999 40.999 50.999 7
      MSAM0.133 10.030 20.028 70.024 80.016 90.012 2
      MPSNR11.2637.6141.6440.9541.0643.28
      I=90r=0.65MSSIM0.121 20.856 70.918 80.935 40.944 80.976 4
      MSAM0.449 90.200 30.155 20.146 80.111 20.106 8
      MPSNR9.5133.5840.9140.2838.1143.02
      r=0.95MSSIM0.077 90.648 80.926 50.921 90.801 70.973 3
      MSAM0.728 40.223 60.166 50.158 90.185 20.110 1
      非周期MPSNR20.8240.3349.2448.6150.7253.25
      r=0.15MSSIM0.556 60.998 40.999 10.998 90.999 20.999 5
      MSAM0.290 90.096 70.095 10.095 50.086 50.031 1
      I=60MPSNR14.4439.9546.8649.3749.8252.19
      r=0.65MSSIM0.210 10.959 80.978 50.986 60.987 10.991 3
      MSAM0.594 20.216 90.171 90.138 20.126 40.101 7
      MPSNR12.7938.3947.1647.8141.9552.64
      r=0.95MSSIM0.153 70.866 90.924 10.939 20.903 50.970 1
      MSAM0.679 60.211 80.176 30.160 80.173 70.115 6
      MPSNR17.3140.6750.6850.9451.3753.84
      r=0.15MSSIM0.450 20.985 70.986 40.987 20.988 70.992 8
      MSAM0.399 80.102 80.101 40.103 50.098 50.087 2
      MPSNR10.9237.6243.2444.8445.3747.91
      I=90r=0.65MSSIM0.113 40.840 80.902 50.911 60.933 10.952 5
      MSAM0.771 40.238 10.182 50.159 80.134 90.118 2
      MPSNR9.2633.1238.8538.1137.5443.86
      r=0.95MSSIM0.072 00.833 60.905 30.899 40.798 70.964 8
      MSAM0.863 20.259 10.168 40.167 20.192 90.120 1
    • Table 3. Comparison of MICV and MMRD values with no-reference evaluation index with real image

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      Table 3. Comparison of MICV and MMRD values with no-reference evaluation index with real image

      数据集指标LRMIDDL0RBSRLRTDNLLRTDTV
      “高分五号”MICV76.8184.6871.9487.4295.68
      MMRD0.11360.09610.09830.08670.0802
      EO-1 HyperionMICV62.8163.1765.7964.5971.35
      MMRD0.05240.04870.04690.04570.0403
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    Xiangyang KONG, Jiao ZHANG, Hui WANG, Baogen XU. Hyperspectral Image Destriping Method Based on Nonlocal Low-Rank and Total Variation[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(5): 64

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    Paper Information

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    Received: Oct. 18, 2023

    Accepted: --

    Published Online: Nov. 13, 2024

    The Author Email:

    DOI:10.3969/j.issn.1009-8518.2024.05.007

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