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无先验水下主动偏振成像方法能够实现目标信息光偏振度和后向散射光偏振度的自动获取, 但该方法在反演过程中仅追寻高对比度这一单一指标, 有时会导致自动获取的两项偏振反演参数过于相近, 使图像复原效果不理想, 且常伴有大量噪声. 针对上述仅追求单一指标导致复原图像质量不理想的问题, 本文提出一种基于双层多指标优化的水下偏振成像方法. 首先, 第1层以互信息和对比度为目标函数, 基于多目标遗传优化算法自动获取偏振参数最优解集; 其次, 选择信息熵为第2层目标函数, 遍历最优解集, 获取偏振参数最终解, 并将其代入成像模型, 获取复原图像; 最终, 根据所获偏振参数之差, 选取适当数字图像处理手段进一步提升复原图像质量. 实验结果表明, 无论背景区域存在与否, 无论目标物偏振度高低, 本方法均能有效增强图像细节, 平衡各项图像质量评价指标, 得到综合质量较高的复原图像.
Underwater imaging is of great significance in exploring seabed resource , monitoring marine environment, implementing underwater rescue and military reconnaissance, etc. by providing clear vison. Among various underwater imaging techniques, the polarization imaging is considered to be an effective way to improve the quality of underwater imaging. It can realize underwater image restoration by using the difference in polarization characteristic between the target light and backscattered light. A classical underwater active polarization imaging method was presented by Treibitz [Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385 ], in which the degrees of linear polarization (DoLPs) of target light and backscattered light are used to recover clear image. A variety of improved methods have been derived from this, but most of them require background areas and human-computer interaction. Then, a new underwater active polarization imaging method without prior knowledge was presented by Zhao [Zhao Y, He W, Ren H, Li Y, Fu Y2022 Opt. Lasers Eng. 148 106777 ], in which the DoLPs of target light and backscattered light can be automatically obtained without background region. However, sometimes the above two parameters are very close and thus introduce a lot of noise into the restored images, for this method takes only the contrast into account.In this work, an underwater active polarization imaging method based on two-layer multi-index optimization is proposed. First, the mutual information and contrast are taken as the upper objective functions, and the Pareto optimal solution set is obtained by the multi-objective genetic optimization algorithm. Second, the information entropy is taken as the lower objective function to obtain the optimal parameters from this optimal solution set. Based on the optimal parameters, the restored images are obtained. According to the difference between the DoLPs of target light and backscattered light, these restored images are further improved by the digital image processing method. The experimental results indicate that our method can not only enhance image details effectively but also balance various evaluation indexes of the imaging quality to obtain high-quality restored images. The proposed algorithm is suitable for underwater targets with low and high DoLPs, with or without background regions. -
Keywords:
- underwater imaging /
- polarization /
- scattering /
- two-layer multi-index optimization
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图 2 双层多指标优化算法流程图
Fig. 2. Flow chart of the two-layer multi-index optimization algorithm.
图 3 水下偏振成像实验装置示意图
Fig. 3. Schematic of experimental setup for underwater polarization imaging.
图 4 最优解集中不同解值的复原结果对比 (a1)—(d1) 不同解值的目标信息光图像; (a2)—(d2) 不同解值的后向散射光图像
Fig. 4. Comparison of restoration results with different solution values: (a1)–(d1) Images of target signal light; (a2)–(d2) images of backscattered light.
图 6 图5划线处光强值分布曲线图 (a) 玩具图像划线处; (b) 金属尺图像划线处
Fig. 6. Intensity distribution at the scribe lines of Fig. 5: (a) Scribe line of toy; (b) scribe line of metal ruler.
图 7 无背景图像复原结果对比
Fig. 7. Comparison of restoration results of images without background.
表 1 不同解值复原图像的客观指标评价结果
Table 1. Objective index evaluation results of restored images with different solution values.
Index (a) (b) (c) (d) MI(B, S) 2.529 2.969 2.062 3.093 C(S) 0.203 0.279 0.067 0.306 Entropy(S) 4.834 4.695 4.455 4.953 
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表 2 含背景图像偏振反演参数对比
Table 2. Comparison of polarization inversion parameters of restored images with background.
Parameter Toy Ruler Cube-coin Treibitz Zhao Our Treibitz Zhao Our Treibitz Zhao Our Pobj 0.190 0.249 0.248 1 0.503 0.501 1 0.668 0.649 Pscat 0.291 0.271 0.272 0.479 0.495 0.445 0.659 0.651 0.586 |Pdif| 0.101 0.022 0.024 0.521 0.008 0.056 0.341 0.017 0.063
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表 3 含背景复原图像的客观评价结果
Table 3. Objective index evaluation results of restored images with background.
Image SNR Entropy Contrast NIQE Toy-Intensity 15.607 5.373 0.033 9.208 Toy-Treibitz 4.120 5.949 0.096 11.008 Toy-Zhao 3.269 5.183 0.319 11.889 Toy-Our 3.653 7.279 0.287 8.989 Ruler-Intensity 24.047 3.826 0.011 10.126 Ruler-Treibitz 3.368 3.059 0.008 8.337 Ruler-Zhao 2.954 2.079 0.448 15.465 Ruler-Our 6.318 6.971 0.138 14.719 Cube-coin-Intensity 17.751 4.951 0.018 7.982 Cube-coin-Treibitz 1.281 3.294 0.014 7.046 Cube-coin-Zhao 2.535 2.532 0.407 12.732 Cube-coin-Our 7.028 7.635 0.178 12.154
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表 4 无背景复原图像的客观评价结果
Table 4. Objective index evaluation results of restored images without background.
Image SNR Entropy Contrast NIQE Toy-Intensity 17.101 5.319 0.029 9.006 Toy-CLAHE 13.283 6.214 0.059 10.357 Toy-Zhao 6.314 6.083 0.127 15.829 Toy-Our 5.269 6.914 0.145 16.152 Ruler-Intensity 15.794 6.284 0.062 9.391 Ruler-CLAHE 15.389 6.442 0.067 10.395 Ruler-Zhao 3.424 1.154 0.492 15.277 Ruler-Our 9.889 7.421 0.128 19.501 Cube-coin-Intensity 16.477 5.313 0.023 7.727 Cube-coin-CLAHE 14.139 6.048 0.041 9.149 Cube-coin-Zhao 2.210 1.828 0.438 12.893 Cube-coin-Our 3.477 6.753 0.187 12.269
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表 5 无背景复原图像偏振反演参数对比
Table 5. Comparison of polarization inversion parameters of restored images without background.
Parameter Toy Ruler Cube-coin Zhao Our Zhao Our Zhao Our Pobj 0.223 0.199 0.328 0.334 0.656 0.687 Pscat 0.271 0.254 0.319 0.265 0.643 0.613 |Pdif| 0.048 0.055 0.009 0.069 0.013 0.074
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[1] Komatsu S, Markman A, Javidi B 2018 Opt. Lett. 43 3261
Google Scholar
[2] Panetta K, Gao C, Agaian S 2015 IEEE J. Ocean. Eng. 41 541
Google Scholar
[3] Gao S B, Zhang M, Zhao Q, Zhang X S, Li Y J 2019 IEEE Trans. Image Process. 28 5580
Google Scholar
[4] Bailey G N, Flemming N C 2008 Quat. Sci. Rev. 27 2153
Google Scholar
[5] 刘飞, 孙少杰, 韩平丽, 赵琳, 邵晓鹏 2021 物理学报 70 164201
Google Scholar
Liu F, Sun S J, Han P L, Zhao L, Shao X P 2021 Acta Phys. Sin. 70 164201
Google Scholar
[6] 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–July 2, 2004 p536
[7] Li N, Zhao Y, Pan Q, Kong S 2018 Opt. Express 26 16488
Google Scholar
[8] Schechner Y Y, Karpel N 2005 IEEE J. Ocean. Eng. 30 570
Google Scholar
[9] Schechner Y Y, Narasimhan S G, Nayar S K 2003 Appl. Opt. 42 511
Google Scholar
[10] Jaffe J S 1990 IEEE J. Ocean. Eng. 15 101
Google Scholar
[11] Treibitz T, Schechner Y Y 2009 IEEE Trans. Pattern Anal. Mach. Intell. 31 385
Google Scholar
[12] Li X, Hu H, Zhao L, Wang H, Yu Y, Wu L, Liu T 2018 Sci. Rep. 8 12430
Google Scholar
[13] 封斐, 吴国俊, 吴亚风, 苗宇宏, 刘博 2020 光学学报 40 2111002
Google Scholar
Feng F, Wu G J, Wu Y F, Miao Y H, Liu B 2020 Acta Optica Sin. 40 2111002
Google Scholar
[14] Zhao Y, He W, Ren H, Li Y, Fu Y 2022 Opt. Lasers Eng. 148 106777
Google Scholar
[15] Yang L, Liang J, Zhang W, Ju H, Ren L, Shao X 2019 Opt. Commun. 438 96
Google Scholar
[16] Rowe M P, Pugh E N, Tyo J S, Engheta N 1995 Opt. Lett. 20 608
Google Scholar
[17] 管今哥, 朱京平, 田恒, 侯洵 2015 物理学报 64 224203
Google Scholar
Guan J G, Zhu J P, Tian H, Hou X 2015 Acta Phys. Sin. 64 224203
Google Scholar
[18] 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
[19] Holland J H 1992 Sci. Am. 267 66
Google Scholar
[20] Deb K, Pratap A, Agarwal S, Meyarivan T 2002 IEEE Trans. Evolut. Comput. 6 182
Google Scholar
[21] Sardy S, Tseng P, Bruce A. 2001 IEEE Trans. Signal Process. 49 1146
Google Scholar
[22] Reza A 2004 VLSI Signal Process. 38 35
Google Scholar
[23] Mittal A, Soundararajan R, Bovik A C 2012 IEEE Signal Process. Lett. 20 209
Google Scholar
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