Chinese Optics, Volume. 16, Issue 5, 1056(2023)

Multispectral demosaicing method based on an improved guided filter

Hai-chao QI1, Yan-song SONG1,2、*, Bo ZHANG1, Zong-lin LIANG1, Gang-qi YAN1, Jia-yin XUE2, Yi-qun ZHANG2, and Bin REN3
Author Affiliations
  • 1Institute of Space Photoelectric Technology, School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China
  • 2Peng Cheng Laboratory, Shenzhen 518052, China
  • 3Xi’an Branch of China Academy of Space Technology, Xi’an 710212, China
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    Figures & Tables(11)
    (a) Binary tree splitting process and (b) arrangement of Five-band MSFA
    Pixel arrangement in neighborhood T
    Autoregressive model in the horizontal-vertical direction
    Estimation of model parameter
    Demosaicing process based on weight-guided filtering
    Comparison of Balloons images reconstructed by different algorithms
    Comparison of CD images reconstructed by different algorithms
    Comparison of party images reconstructed by different algorithms
    • Table 1. Objective evaluation metrics of the three methods on the CAVE dataset

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

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

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

      数据集GFAPMIDPro
      CAVE1.490.711.33
      TokyoTech1.360.561.29
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    Hai-chao QI, Yan-song SONG, Bo ZHANG, Zong-lin LIANG, Gang-qi YAN, Jia-yin XUE, Yi-qun ZHANG, Bin REN. Multispectral demosaicing method based on an improved guided filter[J]. Chinese Optics, 2023, 16(5): 1056

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    Paper Information

    Category: Original Article

    Received: Nov. 13, 2022

    Accepted: --

    Published Online: Oct. 27, 2023

    The Author Email:

    DOI:10.37188/CO.2022-0231

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