Acta Physica Sinica, Volume. 69, Issue 18, 184202-1(2020)

Multiple aperture underwater imaging algorithm based on polarization information fusion

Bin Liu, Peng-Xiang Zhao, Xia Zhao*, Yue Luo, and Li-Chao Zhang
Author Affiliations
  • School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
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    Figures & Tables(7)
    Simulated turbid underwater multiple aperture imaging experiment scene.
    Comparison of simulated turbid underwater target image recovery results. Up panel: Doll image recovery results. Down panel: Metal coin image recovery results. (a1), (a2) The polarization degree distribution and background scattering estimated by the fitting method; (b1), (b2) refocusing estimation of polarization degree distribution and background scattering; (c1), (c2) horizontally polarized image; (d1), (d2) vertically polarized images; (e1), (e2) image restoration by fitting method; (f1), (f2) image recovery from reference perspective; (g1), (g2) multi-perspective fusion to restore the image.
    Comparison of simulated different turbid concentrations underwater target image recovery results. Up panel: Doll image recovery results. Down panel: Metal coin image recovery results. (a1), (a2) Images in clear water; (b1), (b2) the restoration image corresponding to concentration 1; (c1), (c2) the restoration image corresponding to concentration 2; (d1), (d2) the restoration image corresponding to concentration 3; (e1), (e2) the restoration image corresponding to concentration 4.
    Comparison of simulated underwater target image recovery results with different sediment concentrations. Up panel: Doll image recovery results. Down panel: Metal coin image recovery results. (a1), (a2) Image of clear water; (b1), (b2) the restoration image corresponding to sediment concentration 1; (c1), (c2) the restoration image corresponding to sediment concentration 2; (d1), (d2) the restoration image corresponding to sediment concentration 3.
    • Table 1. Quantitative comparison of experiment 1 results.

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      Table 1. Quantitative comparison of experiment 1 results.

      目标PSNRSSIM
      拟合法参数估计 重建结果 参考视角 恢复结果 多视角融合 重建结果 拟合法参数估计 重建结果 参考视角 恢复结果 多视角融合 重建结果
      玩偶21.431422.524225.82400.73300.83590.8885
      金属币23.069623.958524.55810.72320.83250.8796
    • Table 2. Quantitative comparison of experiment 2 results.

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      Table 2. Quantitative comparison of experiment 2 results.

      目标PSNRSSIM
      浓度1浓度2浓度3浓度4浓度1浓度2浓度3浓度4
      玩偶25.728325.196024.480623.49750.84910.83950.82610.8175
      金属币25.017223.843922.746621.92430.83800.82200.81720.8041
    • Table 3. Quantitative comparison of experiment 3 results.

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      Table 3. Quantitative comparison of experiment 3 results.

      目标PSNRSSIM
      泥沙浓度1泥沙浓度2泥沙浓度3泥沙浓度1泥沙浓度2泥沙浓度3
      玩偶24.775523.393021.57480.79450.73030.6863
      金属币23.953122.607220.48580.76100.70300.6184
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    Bin Liu, Peng-Xiang Zhao, Xia Zhao, Yue Luo, Li-Chao Zhang. Multiple aperture underwater imaging algorithm based on polarization information fusion[J]. Acta Physica Sinica, 2020, 69(18): 184202-1

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    Paper Information

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    Received: Mar. 31, 2020

    Accepted: --

    Published Online: Jan. 5, 2021

    The Author Email:

    DOI:10.7498/aps.69.20200471

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