浅海被动水下偏振成像探测方法
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摘要
针对传统被动水下偏振成像方法忽略水体对光的吸收效应,成像结果中存在严重的色彩失真,且并未深入发掘利用背景散射光中包含的场景信息的问题.提出浅海被动水下偏振成像探测方法,该方法从水体中背景散射光的传输特性出发,分析场景深度信息与散射光的物理关系,建立基于深度信息的水下Lambertian反射模型,实现无色彩畸变的水下目标场景清晰成像探测.实验结果表明,该方法能够提供接近水下目标真实色彩、符合人眼视觉特性的清晰探测结果,提高水下成像探测能力.
Underwater imaging is widely applied to mariculture, archaeology, and hydrocarbon exploration, because it can provide the information about visualized target. Among various underwater imaging techniques, polarization imaging is of particular interest to us, due to its simple system structure and low cost. It images the waterbody through using the polarization characteristics of light, specifically, the background light and target light. Active polarization imaging method illuminates a target scene with an artificial polarized light source to provide polarization information for imaging.But in neritic area, active imaging leads to complex light scattering conditions when artificial light and natural light are superimposed together, which further leads to poor image quality. Passive underwater polarization imaging attempts to recover a clear image by utilizing the polarization characteristics of background light and target light. However, serious color cast always appears in the final image, resulting from light absorbed by water, which may further result in target distortion.In this manuscript, we present a passive underwater polarization imaging method for detecting a target in neritic area. A depth-information-based underwater Lambertian reflection model is established by incorporating the depth information into the traditional Lambertian reflection model. First, we attribute the light changes in color and brightness of a Lambertian surface to the spatial variation of the light. According to Lambertian reflection model, the appearance of a target on a detector depends on the light source, the surface reflectance, and the camera sensitivity function. But in underwater imaging, light attenuation at different wavelengths also varies with depth. By analyzing the transmission characteristics of background light in water, we build a physical relationship between the depth information of the scene and the background light. After that, we take the depth information as the weight of light intensity distribution. Then we calculate the product of the light intensity and the camera sensitivity function in the underwater scene according to gray world algorithm, and the real color information of the target can be obtained. Finally, the clear image of an underwater target scene can be obtained, where color cast is calibrated and background light is removed. Underwater experiments are conducted to demonstrate the validity of the proposed method. Besides, the quantitative analyses also verify the improvement of the quality in final target image.Compared with conventional passive underwater polarization imaging methods, the proposed method is capable of detecting targets in various conditions, with the color cast problem solved. It can provide underwater images with better quality and valid detailed information. Furthermore, the proposed method is easy to conduct with no need to change the conventional polarization imaging system and is promising in various practical applications.
Underwater imaging is widely applied to mariculture, archaeology, and hydrocarbon exploration, because it can provide the information about visualized target. Among various underwater imaging techniques, polarization imaging is of particular interest to us, due to its simple system structure and low cost. It images the waterbody through using the polarization characteristics of light, specifically, the background light and target light. Active polarization imaging method illuminates a target scene with an artificial polarized light source to provide polarization information for imaging.But in neritic area, active imaging leads to complex light scattering conditions when artificial light and natural light are superimposed together, which further leads to poor image quality. Passive underwater polarization imaging attempts to recover a clear image by utilizing the polarization characteristics of background light and target light. However, serious color cast always appears in the final image, resulting from light absorbed by water, which may further result in target distortion.In this manuscript, we present a passive underwater polarization imaging method for detecting a target in neritic area. A depth-information-based underwater Lambertian reflection model is established by incorporating the depth information into the traditional Lambertian reflection model. First, we attribute the light changes in color and brightness of a Lambertian surface to the spatial variation of the light. According to Lambertian reflection model, the appearance of a target on a detector depends on the light source, the surface reflectance, and the camera sensitivity function. But in underwater imaging, light attenuation at different wavelengths also varies with depth. By analyzing the transmission characteristics of background light in water, we build a physical relationship between the depth information of the scene and the background light. After that, we take the depth information as the weight of light intensity distribution. Then we calculate the product of the light intensity and the camera sensitivity function in the underwater scene according to gray world algorithm, and the real color information of the target can be obtained. Finally, the clear image of an underwater target scene can be obtained, where color cast is calibrated and background light is removed. Underwater experiments are conducted to demonstrate the validity of the proposed method. Besides, the quantitative analyses also verify the improvement of the quality in final target image.Compared with conventional passive underwater polarization imaging methods, the proposed method is capable of detecting targets in various conditions, with the color cast problem solved. It can provide underwater images with better quality and valid detailed information. Furthermore, the proposed method is easy to conduct with no need to change the conventional polarization imaging system and is promising in various practical applications.
引文
[1]Lavest J M,Guichard F,Rousseau C 2002 International Conference on Image Processing Rochester,NY,USA,September 22-25,2002 p813
[2]Panetta K,Gao C,Agaian S 2016 IEEE J.Oceanic Eng.41 541
[3]Chennu A,F?rber P,De’Ath G,de Beer D,Fabricius KE 2017 Sci.Rep.7 7122
[4]Chiang J Y,Chen Y C 2012 IEEE Trans.Image Process.21 1756
[5]Zhao X W,Jin T,Chi H,Qu S 2015 Acta Phys.Sin.64104201(in Chinese)[赵欣慰,金韬,池灏,曲嵩2015物理学报64 104201]
[6]Han P L,Liu F,Zhang G,Tao Y,Shao X P 2018 Acta Phys.Sin.67 054202(in Chinese)[韩平丽,刘飞,张广,陶禹,邵晓鹏2018物理学报67 054202]
[7]Liu F,Cao L,Shao X,Han P L,Bin X 2015 Appl.Opt.54 8116
[8]Huang B J,Liu T G,Han H F,Han J H,Yu M X 2016Opt.Express 24 9826
[9]Schechner Y Y,Karpel N 2005 IEEE J.Oceanic Eng.30 570
[10]Han P L,Liu F,Yang K,Ma J Y,Li J J,Shao X P 2017Appl.Opt.56 6631
[11]Schechner Y Y,Averbuch Y 2007 IEEE Trans.Pattern Anal.Mach.Intell.29 1655
[12]Schechner Y Y,Karpel N 2004 IEEE Computer Vision and Pattern Recognition Washington,USA,June 22-25,2004 p536
[13]Jaffe J S 2010 Opt.Express 18 12328
[14]Guan J G,Zhu J P,Tian H,Hou X 2015 Acta Phys.Sin.64 224203(in Chinese)[管今哥,朱京平,田恒,侯洵2015物理学报64 224203]
[15]Treibitz T,Schechner Y Y 2009 IEEE Trans.Pattern Anal.Mach.Intell.31 385
[16]Liu F,Shao X,Gao Y,Xiang L B,Han P L,Li G 2016J.Opt.Soc.Am.A 33 237
[17]Ellis J W,Bath J 1938 J.Chem.Phys.6 723
[18]Pegau W S,Gray D,Zaneveld J R V 1997 Appl.Opt.36 6035
[19]Pope R M,Fry E S 1997 Appl.Opt.36 8710
[20]Kopelevich O V,Burenkov V I 1977 Oceanology 17 278
[21]Weijer J V D,Gevers T,Gijsenij A 2007 IEEE Trans.Image Process 16 2207
[22]Lee Z,Wei J,Voss K,Lewis M,Bricaud A,Huot Y 2015Appl.Opt.54 546
[23]Le M N,Wang G,Zheng H B,Liu J B,Zhou Y,Xu Z2017 Opt.Express 25 22859
[24]Dubreuil M,Delrot P,Leonard I,Alfalou A,Brosseau C,Dogariu A 2013 Appl.Opt.52 997
[25]Piederrière Y,Boulvert F,Cariou J,Jeune B L,Guern Y,Brun G L 2005 Opt.Express 13 5030
[26]Li F,Wu J,Wang Y,Zhao Y,Zhang X 2012 IEEE Fifth International Conference on Advanced Computational Intelligence Nanjing,China,March 29-31,2012 p662
[2]Panetta K,Gao C,Agaian S 2016 IEEE J.Oceanic Eng.41 541
[3]Chennu A,F?rber P,De’Ath G,de Beer D,Fabricius KE 2017 Sci.Rep.7 7122
[4]Chiang J Y,Chen Y C 2012 IEEE Trans.Image Process.21 1756
[5]Zhao X W,Jin T,Chi H,Qu S 2015 Acta Phys.Sin.64104201(in Chinese)[赵欣慰,金韬,池灏,曲嵩2015物理学报64 104201]
[6]Han P L,Liu F,Zhang G,Tao Y,Shao X P 2018 Acta Phys.Sin.67 054202(in Chinese)[韩平丽,刘飞,张广,陶禹,邵晓鹏2018物理学报67 054202]
[7]Liu F,Cao L,Shao X,Han P L,Bin X 2015 Appl.Opt.54 8116
[8]Huang B J,Liu T G,Han H F,Han J H,Yu M X 2016Opt.Express 24 9826
[9]Schechner Y Y,Karpel N 2005 IEEE J.Oceanic Eng.30 570
[10]Han P L,Liu F,Yang K,Ma J Y,Li J J,Shao X P 2017Appl.Opt.56 6631
[11]Schechner Y Y,Averbuch Y 2007 IEEE Trans.Pattern Anal.Mach.Intell.29 1655
[12]Schechner Y Y,Karpel N 2004 IEEE Computer Vision and Pattern Recognition Washington,USA,June 22-25,2004 p536
[13]Jaffe J S 2010 Opt.Express 18 12328
[14]Guan J G,Zhu J P,Tian H,Hou X 2015 Acta Phys.Sin.64 224203(in Chinese)[管今哥,朱京平,田恒,侯洵2015物理学报64 224203]
[15]Treibitz T,Schechner Y Y 2009 IEEE Trans.Pattern Anal.Mach.Intell.31 385
[16]Liu F,Shao X,Gao Y,Xiang L B,Han P L,Li G 2016J.Opt.Soc.Am.A 33 237
[17]Ellis J W,Bath J 1938 J.Chem.Phys.6 723
[18]Pegau W S,Gray D,Zaneveld J R V 1997 Appl.Opt.36 6035
[19]Pope R M,Fry E S 1997 Appl.Opt.36 8710
[20]Kopelevich O V,Burenkov V I 1977 Oceanology 17 278
[21]Weijer J V D,Gevers T,Gijsenij A 2007 IEEE Trans.Image Process 16 2207
[22]Lee Z,Wei J,Voss K,Lewis M,Bricaud A,Huot Y 2015Appl.Opt.54 546
[23]Le M N,Wang G,Zheng H B,Liu J B,Zhou Y,Xu Z2017 Opt.Express 25 22859
[24]Dubreuil M,Delrot P,Leonard I,Alfalou A,Brosseau C,Dogariu A 2013 Appl.Opt.52 997
[25]Piederrière Y,Boulvert F,Cariou J,Jeune B L,Guern Y,Brun G L 2005 Opt.Express 13 5030
[26]Li F,Wu J,Wang Y,Zhao Y,Zhang X 2012 IEEE Fifth International Conference on Advanced Computational Intelligence Nanjing,China,March 29-31,2012 p662