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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. -
Keywords:
- underwater imaging /
- polarization characteristic /
- common-mode rejection /
- scattering
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Google Scholar
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Google Scholar
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图 1 浑浊水体散射光场特性分析
Figure 1. Scattering conditions of light at different turbidity.
图 2 不同散射程度的强度变化曲线
Figure 2. Intensity variation in different scattering area.
图 3 水下偏振成像原理图
Figure 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
Figure 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)和图4(d)中第196行像素强度分布; (b) 图4(a), 图4(b), 图4(c)和图4(d)中第253列像素强度分布
Figure 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
Figure 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
Figure 7. Objective evaluation results of underwater images.
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[1] Panetta K, Gao C, Agaian S 2013 IEEE Trans. Consum. Electron. 59 643
Google Scholar
[2] Satoru K, Adam M, Bahram J 2018 Opt. Lett. 43 3261
Google Scholar
[3] Ji T, Wang G 2015 J. Ocean Univ. Chin. 14 255
Google 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 78
Google Scholar
Hu H F, Li X B, Liu T G 2019 Infrared Laser Eng. 48 78
Google Scholar
[6] Jaffe J S 1990 IEEE J. Ocean. Eng. 15 101
Google Scholar
[7] 韩平丽, 刘飞, 张广, 陶禹, 邵晓鹏 2018 67 054202
Google Scholar
Han P L, Liu F, Zhang G, Tao Y, Shao X P 2018 Acta Phys. Sin. 67 054202
Google 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 3502
Google Scholar
[11] Li X, Hu H, Zhao L, Wang H, Yu Y, Wu L, Liu T 2018 Sci. Rep. 8 12430
Google Scholar
[12] Emberton S, Chittka L, Cavallaro A 2018 Comput. Vision Image Understanding 168 145
Google Scholar
[13] Gao S B, Zhang M, Zhao Q, Zhang X S, Li Y J 2019 IEEE Trans. Image Process. 28 5580
Google 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 67 184202
Google Scholar
Wei Y, Liu F, Yang K, Han P L, Wang X H, Shao X P 2018 Acta Phys. Sin. 67 184202
Google Scholar
[16] Mclean E A, Burris H R, Strand M P 1995 Appl. Opt. 34 4343
Google 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 13 738
Google Scholar
[19] Hu H, Zhao L, Li X, Wang H, Yang J, Li K, Liu T 2018 Opt. Express 26 25047
Google Scholar
[20] Liu F, Wei Y, Han P, Yang K, Bai L, Shao X 2019 Opt. Express 27 3629
Google Scholar
[21] Han P, Liu F, Yang K, Ma J, Li J, Shao X 2017 Appl. Opt. 56 6631
Google Scholar
[22] Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385
Google Scholar
[23] Huang B, Liu T, Hu H, Han J, Yu M 2016 Opt. Express 24 9826
Google Scholar
[24] Hu H, Zhao L, Huang B, Li X, Wang H, Liu T 2017 IEEE Photonics J. 9 1
Google Scholar
[25] Liu F, Han P, Wei Y, Yang K, Huang S, Li X, Zhang G, Bai L, Shao X 2018 Opt. Lett. 43 4903
Google Scholar
[26] Ntziachristos V 2010 Nat. Methods 7 603
Google Scholar
[27] Candes E J, Xiaodong L I, Yl M A, Wright J 2011 J. ACM 58 1
Google Scholar
[28] Chandrasekaran V, Sanghavi S, Parrilo P A, Willsky A S 2009 SIAM J. Optim. 21 572
Google Scholar
[29] Hu Y, Zhang D, Ye J, Li X, He X 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 2117
Google Scholar
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