Optics and Precision Engineering, Volume. 31, Issue 16, 2418(2023)

Superresolution reconstruction of infrared polarization microscan images in focal plane

Yizhe MA1...2,3, Shiyong WANG1,2,3, Teng LEI1,2,3, Bohan LI1,2,3, and Fanming LI1,23,* |Show fewer author(s)
Author Affiliations
  • 1Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Laboratory of Infrared Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 2008, China
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    Figures & Tables(14)
    Schematic diagram of microscanning
    Test set
    Schematic diagram of microscan transformation
    False deviation of micropolarization unit displacement
    False bias caused by noise
    Influence of noise on polarization degree solution
    Comparison of the reconstructed result of Macbeth_Enhancement with superresolution (a) Reference image (b) Real HR image (c) Bilinear interpolation (d) Bicubic interpolation (e) Cubic spline interpolation (f)NP result (g)EARI result (h)POCS result (i)FPPOCS result (j) The algorithm in this paper
    Noise robustness of each algorithm
    Schematic diagram of the polarization microscan detection system in the HF infrared focal plane
    Superresolution reconstruction performance comparison
    Comparison of polarization superresolution reconstruction results of buildings
    • Table 1. Comparison of reconstruction effects of different algorithms in Macbeth_Enhancement

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      Table 1. Comparison of reconstruction effects of different algorithms in Macbeth_Enhancement

      AlgorithmSSIMPSNR/dBRMSE
      Bilinear0.561 226.245 40.048 7
      Bicubic0.628 826.344 80.048 1
      Spline0.608 525.787 80.051 3
      NP0.592 122.677 20.073 5
      EARI0.581 423.258 80.068 7
      POCS0.628 026.319 80.048 3
      FPPOCS0.631 527.005 20.044 6
      OURS0.685 230.165 10.031 0
    • Table 2. Test results of different algorithms on different test sets

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      Table 2. Test results of different algorithms on different test sets

      Data_NameBilinearBicubicSplineNPEARIPOCSFPPOCSOURS
      FoodSSIM0.678 80.683 60.666 80.759 80.677 00.683 60.807 20.825 7
      PSNR/dB28.937 628.331 127.680 230.543 227.951 728.331 234.064 736.289 4
      RMSE0.035 70.038 30.041 30.029 70.040 00.038 30.019 80.015 3
      GlassSSIM0.869 20.911 30.904 20.875 60.828 40.910 60.901 20.920 7
      PSNR/dB36.192 438.051 037.555 632.977 532.061 737.773 437.521 638.373 9
      RMSE0.015 50.012 50.013 30.022 40.024 90.012 90.013 30.012 1
      LeavesSSIM0.373 70.382 00.366 20.472 50.380 20.378 70.524 20.605 4
      PSNR/dB18.964 118.099 517.392 323.860 120.734 718.119 023.933 430.380 7
      RMSE0.112 70.124 50.135 00.064 10.091 90.124 20.063 60.030 3
      LiquidSSIM0.630 40.660 40.638 30.681 30.597 40.658 30.680 70.789 7
      PSNR/dB28.757 228.816 128.200 428.010 826.103 028.602 928.854 731.890 3
      RMSE0.036 50.036 20.038 90.039 80.074 60.037 10.036 10.025 4
      Macbeth_ClassicSSIM0.582 30.616 80.597 40.571 10.553 30.616 10.700 10.728 1
      PSNR/dB25.260 624.793 824.121 523.285 622.546 924.805 528.321 831.707 7
      RMSE0.054 60.057 60.062 20.068 50.074 60.057 50.038 40.026 0
    • Table 3. FIG. 11 Running time of each algorithm

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      Table 3. FIG. 11 Running time of each algorithm

      AlgorithmBilinearBicubicSplineNewton
      Time/s0.0210.0170.0380.228
      AlgorithmEARIPOCSFPPOCSOURS
      Time/s0.2931.8592.07425.863
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    Yizhe MA, Shiyong WANG, Teng LEI, Bohan LI, Fanming LI. Superresolution reconstruction of infrared polarization microscan images in focal plane[J]. Optics and Precision Engineering, 2023, 31(16): 2418

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    Paper Information

    Category: Information Sciences

    Received: Feb. 13, 2023

    Accepted: --

    Published Online: Sep. 5, 2023

    The Author Email: LI Fanming (lfmjws@163.com)

    DOI:10.37188/OPE.20233116.2418

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