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基于稀疏低秩特性的水下非均匀光场偏振成像技术研究

刘飞 孙少杰 韩平丽 赵琳 邵晓鹏

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基于稀疏低秩特性的水下非均匀光场偏振成像技术研究

刘飞, 孙少杰, 韩平丽, 赵琳, 邵晓鹏

Clear underwater vision in non-uniform scattering field by low-rank-and-sparse-decomposition-based olarization imaging

Liu Fei, Sun Shao-Jie, Han Ping-Li, Zhao Lin, Shao Xiao-Peng
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  • 针对浑浊水体偏振成像时由于强散射作用导致的背景散射光分布不均匀且目标信息被淹没, 无法有效解译, 难以实现清晰化成像的问题, 提出基于稀疏低秩特性的水下非均匀光场偏振成像技术. 该技术利用散射光场中偏振信息的共模抑制特性消除非均匀性, 结合水下散射光场中背景信息纹理单一、信息相关性高以及目标信息空间占比小的特点, 建立偏振域的稀疏-低秩信息分析处理模型, 有效分离目标和背景信息, 重建高对比度清晰目标图像. 实验结果表明, 基于稀疏低秩特性的水下非均匀光场偏振成像技术不仅能够有效地提升浑浊水下图像的对比度, 复原细节信息, 而且能够有效地抑制非均匀强散射, 在水下偏振成像领域具有良好的应用前景.
    Underwater imaging plays a critical role in marine rescue, seabed resource exploration, underwater archaeology, etc. by providing human-vision-system-friendly information. A variety of approaches have been exploited to realize clear underwater imaging. Noticeably, underwater polarization imaging has attracted attention due to its simple imaging system and clear vision. It can remove the backscattered light from degraded image and recover abundant high-fidelity information of target. Descattering is conducted by using the difference in polarization characteristics between the target and background. A classical underwater polarization imaging method is presented by Schechner [Tali T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385], in which the differential polarization characteristics of backscattered light and target light are used to recover clear image. More researches were conducted including Huang et al.’s research [Huang B J, Liu T G, Hu H F, et al. 2016 Optics Express 24 9826], Liu et al.’s study [Liu F, Han P L, Wei Y, et al. 2018 Opt. Lett. 43 4903], etc.However, in the polarization imaging methods, the uniform underwater backscattered light and polarization parameters over the whole image are usually assumed. In most practical applications, these assumptions cannot hold true. Therefore, the inaccurate estimation of backscattered light makes it difficult to completely descatter an image, leading many methods to fail to detect the target in non-uniform turbid water.In this study, we propose a low-rank-and-sparse-decomposition-based polarization imaging combined with common mode rejection feature of polarization information in scattered light field to eliminate non-uniformity and scattering caused by severe scattering during active polarization imaging of turbid water. The backscattered light is highly reduced and the information contained in background is single and highly correlated. It conforms to the low-rank characteristics of the image. What is more, the target in underwater scene occupies a relatively small proportion, which conforms to the sparsity characteristics of the image. Therefore, combining the low-rank characteristics of backscattered light with the sparse characteristics of target information light, we separate them through low-rank and sparse matrix decomposition to recover clear underwater image. Both experimental and objective image quality evaluation results demonstrate the validity of the proposed method.The proposed method works well in improving polarization vision in non-uniform turbid water, which is due to its ability to make the underwater scene uniform and the target and background information separated through their distribution difference of polarization characteristics. It possesses potential applications in turbid water imaging.
      通信作者: 邵晓鹏, xpshao@xidian.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 62075175, 62005203)和中国科学院光束控制重点实验室基金(批准号: QC20191097)资助的课题
      Corresponding author: Shao Xiao-Peng, xpshao@xidian.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62075175, 62005203), and the Foundation of the Key Laboratory of Optical Engineering, Chinese Academic of Sciences (Grant No. QC20191097)
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    Panetta K, Gao C, Agaian S 2013 IEEE Trans. Consum. Electron. 59 643Google Scholar

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    Satoru K, Adam M, Bahram J 2018 Opt. Lett. 43 3261Google Scholar

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    Ji T, Wang G 2015 J. Ocean Univ. Chin. 14 255Google Scholar

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    Schechner Y Y, Karpel N 2004 Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Washington DC, USA Jun. 27−Jul. 2, 2004 p536

    [5]

    胡浩丰, 李校博, 刘铁根 2019 红外与激光工程 48 78Google Scholar

    Hu H F, Li X B, Liu T G 2019 Infrared Laser Eng. 48 78Google Scholar

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    Jaffe J S 1990 IEEE J. Ocean. Eng. 15 101Google Scholar

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    韩平丽, 刘飞, 张广, 陶禹, 邵晓鹏 2018 物理学报 67 054202Google Scholar

    Han P L, Liu F, Zhang G, Tao Y, Shao X P 2018 Acta Phys. Sin. 67 054202Google Scholar

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    李庆忠, 葛中峰 2011 光电子·激光 22 1862

    Li Q Z, Ge Z F 2011 J. Optoelectron.·Laser 22 1862

    [9]

    Nascimento E, Campos M, Barros W 2009 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing Rio de Janiero, Brazil, Oct. 11−15, 2009 p330

    [10]

    Wang Z, Liu H, Huang N, Zhang Y, Chi J 2019 Opt. Lett. 44 3502Google Scholar

    [11]

    Li X, Hu H, Zhao L, Wang H, Yu Y, Wu L, Liu T 2018 Sci. Rep. 8 12430Google Scholar

    [12]

    Emberton S, Chittka L, Cavallaro A 2018 Comput. Vision Image Understanding 168 145Google Scholar

    [13]

    Gao S B, Zhang M, Zhao Q, Zhang X S, Li Y J 2019 IEEE Trans. Image Process. 28 5580Google Scholar

    [14]

    Singh G, Vasamsetti S, Sardana H, Kumar S, Jaggi N, Mittal N 2015 2015 IEEE Underwater Technology (UT) Chennai, India, Feb. 23−25, 2015 p1

    [15]

    卫毅, 刘飞, 杨奎, 韩平丽, 王新华, 邵晓鹏 2018 物理学报 67 184202Google Scholar

    Wei Y, Liu F, Yang K, Han P L, Wang X H, Shao X P 2018 Acta Phys. Sin. 67 184202Google Scholar

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    Mclean E A, Burris H R, Strand M P 1995 Appl. Opt. 34 4343Google Scholar

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    Narasimhan S G, Nayar S K, Sun B, Koppal S J 2005 Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 Beijing, China, Oct. 17−21, 2005 p420

    [18]

    Jaffe J S 2005 Opt. Express 13 738Google Scholar

    [19]

    Hu H, Zhao L, Li X, Wang H, Yang J, Li K, Liu T 2018 Opt. Express 26 25047Google Scholar

    [20]

    Liu F, Wei Y, Han P, Yang K, Bai L, Shao X 2019 Opt. Express 27 3629Google Scholar

    [21]

    Han P, Liu F, Yang K, Ma J, Li J, Shao X 2017 Appl. Opt. 56 6631Google Scholar

    [22]

    Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385Google Scholar

    [23]

    Huang B, Liu T, Hu H, Han J, Yu M 2016 Opt. Express 24 9826Google Scholar

    [24]

    Hu H, Zhao L, Huang B, Li X, Wang H, Liu T 2017 IEEE Photonics J. 9 1Google Scholar

    [25]

    Liu F, Han P, Wei Y, Yang K, Huang S, Li X, Zhang G, Bai L, Shao X 2018 Opt. Lett. 43 4903Google Scholar

    [26]

    Ntziachristos V 2010 Nat. Methods 7 603Google Scholar

    [27]

    Candes E J, Xiaodong L I, Yl M A, Wright J 2011 J. ACM 58 1Google Scholar

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    Chandrasekaran V, Sanghavi S, Parrilo P A, Willsky A S 2009 SIAM J. Optim. 21 572Google Scholar

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    Hu Y, Zhang D, Ye J, Li X, He X 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 2117Google Scholar

  • 图 1  浑浊水体散射光场特性分析

    Fig. 1.  Scattering conditions of light at different turbidity.

    图 2  不同散射程度的强度变化曲线

    Fig. 2.  Intensity variation in different scattering area.

    图 3  水下偏振成像原理图

    Fig. 3.  Schematic of underwater polarization imaging.

    图 4  水下偏振成像实验结果 (a) 原始强度图像; (b) 偏振共模抑制图像; (c)和(d)分别为文中所述方法分离的背景散射光和目标信息光图像

    Fig. 4.  Experimental results: (a) intensity image; (b) PDI image; (c) and (d) estimated backscattering and object information by the proposed method.

    图 5  实验结果图像像素强度统计 (a) 图4(a), 图4(b), 图4(c)图4(d)中第196行像素强度分布; (b) 图4(a), 图4(b), 图4(c)图4(d)中第253列像素强度分布

    Fig. 5.  Pixel intensity distribution of experimental results: (a) Horizontal line plot of Row 196 from Fig. 4(a), 4(b), 4(c) and 4(d); (b) the vertical line plot of Column 253 from Fig. 4(a), 4(b), 4(c) and 4(d).

    图 6  不同浓度溶液中实验结果 (a1)—(f1) 为原始强度图像; (a2)—(f2) 为重建结果

    Fig. 6.  Results in solutions at different concentrations: (a1)–(f1) Intensity images; (a2)–(f2) reconstructed images.

    图 7  图像质量评价参数的客观评价结果

    Fig. 7.  Objective evaluation results of underwater images.

  • [1]

    Panetta K, Gao C, Agaian S 2013 IEEE Trans. Consum. Electron. 59 643Google Scholar

    [2]

    Satoru K, Adam M, Bahram J 2018 Opt. Lett. 43 3261Google Scholar

    [3]

    Ji T, Wang G 2015 J. Ocean Univ. Chin. 14 255Google Scholar

    [4]

    Schechner Y Y, Karpel N 2004 Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Washington DC, USA Jun. 27−Jul. 2, 2004 p536

    [5]

    胡浩丰, 李校博, 刘铁根 2019 红外与激光工程 48 78Google Scholar

    Hu H F, Li X B, Liu T G 2019 Infrared Laser Eng. 48 78Google Scholar

    [6]

    Jaffe J S 1990 IEEE J. Ocean. Eng. 15 101Google Scholar

    [7]

    韩平丽, 刘飞, 张广, 陶禹, 邵晓鹏 2018 物理学报 67 054202Google Scholar

    Han P L, Liu F, Zhang G, Tao Y, Shao X P 2018 Acta Phys. Sin. 67 054202Google Scholar

    [8]

    李庆忠, 葛中峰 2011 光电子·激光 22 1862

    Li Q Z, Ge Z F 2011 J. Optoelectron.·Laser 22 1862

    [9]

    Nascimento E, Campos M, Barros W 2009 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing Rio de Janiero, Brazil, Oct. 11−15, 2009 p330

    [10]

    Wang Z, Liu H, Huang N, Zhang Y, Chi J 2019 Opt. Lett. 44 3502Google Scholar

    [11]

    Li X, Hu H, Zhao L, Wang H, Yu Y, Wu L, Liu T 2018 Sci. Rep. 8 12430Google Scholar

    [12]

    Emberton S, Chittka L, Cavallaro A 2018 Comput. Vision Image Understanding 168 145Google Scholar

    [13]

    Gao S B, Zhang M, Zhao Q, Zhang X S, Li Y J 2019 IEEE Trans. Image Process. 28 5580Google Scholar

    [14]

    Singh G, Vasamsetti S, Sardana H, Kumar S, Jaggi N, Mittal N 2015 2015 IEEE Underwater Technology (UT) Chennai, India, Feb. 23−25, 2015 p1

    [15]

    卫毅, 刘飞, 杨奎, 韩平丽, 王新华, 邵晓鹏 2018 物理学报 67 184202Google Scholar

    Wei Y, Liu F, Yang K, Han P L, Wang X H, Shao X P 2018 Acta Phys. Sin. 67 184202Google Scholar

    [16]

    Mclean E A, Burris H R, Strand M P 1995 Appl. Opt. 34 4343Google Scholar

    [17]

    Narasimhan S G, Nayar S K, Sun B, Koppal S J 2005 Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 Beijing, China, Oct. 17−21, 2005 p420

    [18]

    Jaffe J S 2005 Opt. Express 13 738Google Scholar

    [19]

    Hu H, Zhao L, Li X, Wang H, Yang J, Li K, Liu T 2018 Opt. Express 26 25047Google Scholar

    [20]

    Liu F, Wei Y, Han P, Yang K, Bai L, Shao X 2019 Opt. Express 27 3629Google Scholar

    [21]

    Han P, Liu F, Yang K, Ma J, Li J, Shao X 2017 Appl. Opt. 56 6631Google Scholar

    [22]

    Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385Google Scholar

    [23]

    Huang B, Liu T, Hu H, Han J, Yu M 2016 Opt. Express 24 9826Google Scholar

    [24]

    Hu H, Zhao L, Huang B, Li X, Wang H, Liu T 2017 IEEE Photonics J. 9 1Google Scholar

    [25]

    Liu F, Han P, Wei Y, Yang K, Huang S, Li X, Zhang G, Bai L, Shao X 2018 Opt. Lett. 43 4903Google Scholar

    [26]

    Ntziachristos V 2010 Nat. Methods 7 603Google Scholar

    [27]

    Candes E J, Xiaodong L I, Yl M A, Wright J 2011 J. ACM 58 1Google Scholar

    [28]

    Chandrasekaran V, Sanghavi S, Parrilo P A, Willsky A S 2009 SIAM J. Optim. 21 572Google Scholar

    [29]

    Hu Y, Zhang D, Ye J, Li X, He X 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 2117Google Scholar

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出版历程
  • 收稿日期:  2021-02-10
  • 修回日期:  2021-04-17
  • 上网日期:  2021-06-07
  • 刊出日期:  2021-08-20

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