Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141032(2020)

Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images

Li Wang*, Wei Wang**, and Boni Liu***
Author Affiliations
  • Department of Electronic Engineering, Xi'an Aeronautical University, Xi'an, Shaanxi 710077, China
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    Figures & Tables(11)
    Comparison between proposed HA_OMP and OMP
    Original images of the 50th band. (a) Cuprite1; (b) Cuprite2; (c) Indian Pines; (d) Pavia University
    Influence of evolution generation, population size, and atom number on HA_OMP. (a) Atom number is 50; (b) population size is 10; (c) evolution generation is 5
    Reconstructed PSNR of Cuprite1 vs atom number. (a) OMP; (b) HA_OMP
    Comparison between two reconstructed Cuprite2 images. (a) Reconstructed image of OMP algorithm; (b) reconstructed image of HA_OMP algorithm
    Comparison between two reconstructed Pavia University images. (a) Reconstructed image of OMP algorithm; (b) reconstructed image of HA_OMP algorithm
    • Table 1. Basic situation of four datasets

      View table

      Table 1. Basic situation of four datasets

      SceneOriginalbandsOriginalimage sizeAvailablebandsCroppedimage size
      Cuprite1224614×512188256×256
      Cuprite2224614×512188256×256
      Indian Pines220145×145200128×128
      Pavia University115610×340103256×256
    • Table 2. Optimal PSNR for single band and required atom number

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      Table 2. Optimal PSNR for single band and required atom number

      SceneAlgorithmSR=0.1SR=0.2SR=0.3SR=0.4SR=0.5
      PSNR /dBAtomnumberPSNR /dBAtomnumberPSNR /dBAtomnumberPSNR /dBAtomnumberPSNR /dBAtomnumber
      Cuprite1OMP24.12329.67632.37934.731436.1820
      HA_OMP24.76528.76631.902134.753336.7650
      Cuprite2OMP21.88327.22529.12831.041432.0518
      HA_OMP23.04426.79628.701930.123432.7946
      Indian PinesOMP13.01316.75317.85619.091120.3418
      HA_OMP14.85516.91617.561219.702521.0234
      PaviaUniversityOMP19.78222.03423.54725.381427.0222
      HA_OMP20.24422.57724.051425.792727.2643
    • Table 3. Matching and updating time required for reconstructing single bands

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      Table 3. Matching and updating time required for reconstructing single bands

      SceneAlgorithmTimeSR=0.1SR=0.2SR=0.3SR=0.4SR=0.5
      Cuprite1OMPMatching1031.981054.991049.731063.311086.11
      Updating9.6511.3612.1311.5712.50
      HA_OMPMatching40.0241.7943.5945.2647.52
      Updating1.902.122.202.192.13
      Cuprite2OMPMatching1056.401062.291079.521060.321090.40
      Updating11.3012.7212.1114.6612.65
      HA_OMPMatching39.6242.3544.0845.2746.51
      Updating1.661.882.152.252.28
    • Table 4. Average optimal PSNR of two algorithms under different sampling ratesdB

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      Table 4. Average optimal PSNR of two algorithms under different sampling ratesdB

      SceneAlgorithmSR=0.1SR=0.2SR=0.3SR=0.4SR=0.5
      Cuprite1OMP24.5029.6932.6335.0536.43
      HA_OMP24.5428.9633.0634.7037.19
      Cuprite2OMP21.9627.4929.4231.3432.52
      HA_OMP22.6827.3329.3032.0933.15
      Indian PinesOMP14.9418.8620.2721.7922.93
      HA_OMP17.0919.2121.5121.9023.53
      Pavia UniversityOMP19.2221.8523.3624.9926.48
      HA_OMP20.6422.3823.7425.4326.59
    • Table 5. Acceleration times of HA_OMP relative to OMP under different sampling rates

      View table

      Table 5. Acceleration times of HA_OMP relative to OMP under different sampling rates

      SceneAlgorithmSR=0.1SR=0.2SR=0.3SR=0.4SR=0.5
      Cuprite1OMP11111
      HA_OMP15.108.6212.3912.7111.34
      Cuprite2OMP11111
      HA_OMP15.719.599.8010.8810.42
      Indian PinesOMP11111
      HA_OMP17.6714.119.5811.8211.19
      Pavia UniversityOMP11111
      HA_OMP15.5313.9910.8010.6312.18
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    Li Wang, Wei Wang, Boni Liu. Hermitian Compressed Sensing Reconstruction Algorithm for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141032

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    Paper Information

    Category: Image Processing

    Received: Mar. 6, 2020

    Accepted: Apr. 10, 2020

    Published Online: Jul. 28, 2020

    The Author Email: Wang Li (wangli871016@163.com), Wang Wei (weiiiwang@qq.com), Liu Boni (271629953@qq.com)

    DOI:10.3788/LOP57.141032

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