搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于光场偏振特性的目标表面漫反射分量获取技术

蔡玉栋 韩平丽 刘飞 闫明宇 邵晓鹏

引用本文:
Citation:

基于光场偏振特性的目标表面漫反射分量获取技术

蔡玉栋, 韩平丽, 刘飞, 闫明宇, 邵晓鹏

Polarization-based extracting diffuse reflection from light-field of object surface

Cai Yu-Dong, Han Ping-Li, Liu Fei, Yan Ming-Yu, Shao Xiao-Peng
PDF
HTML
导出引用
  • 针对三维成像、图像匹配及模式识别等领域中目标表面镜面反射光成分影响所导致成像效果受限、特征识别准确度低等问题, 提出一种基于光场偏振特性的目标表面漫反射光分量获取技术. 该技术通过对反射光场中镜面反射与漫反射分量的偏振特性进行深入挖掘, 充分利用各分量之间的偏振差异性及彼此独立性的特点, 建立线性约束模型; 此外, 通过确定线性约束模型的最佳混合系数矩阵, 实现复杂反射光场中对漫反射分量的精确获取和解译. 仿真与真实场景数据处理结果表明, 该技术能够有效地分离复杂光场中的漫反射光分量, 解决了目前三维成像及模式识别技术中对纯漫反射条件的依赖, 为被动式远距离三维成像技术在复杂反射光场中的应用奠定基础.
    The reflection light field of surface of the Non-Lambertian body in nature has both specular reflection and diffuse reflection components. In the process of three-dimensional(3D) reconstruction, image matching and pattern recognition are based on the ideal Lambert body. The imaging effect is limited due to the presence of specular reflection components, and the accuracy of feature recognition is low. In order to obtain the diffuse reflection component accurately, a large number of studies have been conducted for a long time, which can be mainly divided into two parts: intensity- and polarization-based separation techniques. The intensity-based separation algorithm is limited in many aspects due to the prior knowledge, such as light source chromaticity, direction and image color information. With the maturity of detection technology, the acquisition and interpretation of multi-dimensional physical properties of light-field have made great progress of the utilization of polarization characteristics of light wave. Compared with traditional intensity imaging technology, the polarization imaging technology has strongr and many advantages in highlighting targets. However, in traditional polarization-based separation techniques, it is often necessary to assume that the diffuse light is completely unpolarized, which is used in some specific cases but not universally.In this work, we report a method to obtain the diffuse reflection components of the target surface based on the polarization characteristics of the light-field. According to Fresnel's law and Lawrence B. Wolff's reflection model, the reflected light-field on the target surface can be divided into diffuse and specular components with partial polarization. The partial polarization characteristics of diffuse and specular components are explored in depth and the Stokes vector is used to calculate the minimum light intensity of each pixel modulated by polarizer, which is completely unpolarized light. By subtracting completely unpolarized light from the obtained polarized sub-images, the diffuse and specular components in the polarized part satisfy the linear constraint model. Based on the independent component analysis (ICA) model, the diffuse and specular components in the polarized part are regarded as independent and non-interfering additive vectors. The singular value decomposition method and optical relevancy of mutual information are used to determine the optimal mix coefficients matrix of the subcomponents in the linear constrained model. Thus, the diffuse components are accurately acquired and explained from the complex reflected light-field. Simulation and experimental results show that the algorithm mentioned above can accurately obtain the optimal mix coefficients’ matrix without the prior knowledge of illuminant chromaticity, or direction or image chromatic information, or others. This technique can accurately obtain and remove the specular reflection part, at the same time, restore the diffuse light intensity which is covered by the specular reflection and conforms to the change trend of the surface shape. Meanwhile, the pretty good results also demonstrate that the proposed separation method has the strong stability and wide applicability. This technology does not have to make the assumption that 3D imaging technology and computer vision algorithms such as pattern recognition rely on natural objects as ideal Lambert bodies, and it can eleminate the influence of complex reflected light-field on target results, which makes passive remote 3D imaging technology more applicable and more robust.
      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: 61705175)、中国博士后科学基金(批准号: 2017M613063)和中央高校基本科研业务费专项资金(批准号: XJS190502, XJS200505)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 No. 61705175), the China Postdoctoral Science Foundation (Grant No. 2017M613063), and the Fundamental Research Funds for the Central Universities (Grant Nos. XJS190502, XJS200505)
    [1]

    Zhang W Z, Chen Z B, Xia B F, Lin B, Cao X Q 2014 Chin. Phys. B 23 044212Google Scholar

    [2]

    Salvi J, Pages J, Batlle J 2004 Pattern Recognit. 37 827Google Scholar

    [3]

    Varady T, Martin R R, Cox J 1997 Comput.-Aided Des. 29 255Google Scholar

    [4]

    周光照, 王玉丹, 任玉琦, 陈灿, 叶琳琳, 肖体乔 2012 物理学报 61 018701Google Scholar

    Zhou G Z, Wang Y D, Ren Y Q, Chen C, Ye L L, Xiao T Q 2012 Acta Phys. Sin. 61 018701Google Scholar

    [5]

    Zhang R, Tsai P S, Cryer J E, Shah M 1999 IEEE Trans. Pattern Anal. Mach. Intell. 21 690Google Scholar

    [6]

    Jiang L, Zhang J Y, Deng B L, Li H, Liu L G 2018 IEEE Trans. Image Process. 27 4756Google Scholar

    [7]

    Shafer S A 1985 Color Res. Appl. 10 210Google Scholar

    [8]

    Klinker G J, Shafer S A, Kanade T 1988 Int. J. Comput. Vision 2 7Google Scholar

    [9]

    Klinker G J, Shafer S A, Kanade T 1990 Int. J. Comput. Vision 4 7Google Scholar

    [10]

    Novak C L, Shafer S A 1992 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Champaign, USA, June 15–18, 1992 p599

    [11]

    Tan P, Quan L, Lin S 2006 Proceedings 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition New York, USA, June 17–22, 2006 p1855

    [12]

    Shen H L, Zheng Z H 2013 Appl. Opt. 52 4483Google Scholar

    [13]

    Ren W H, Tian J D, Tang Y D 2017 IEEE Trans. Image Process. 26 2327Google Scholar

    [14]

    Sato Y, Ikeuchi K 1994 J. Opt. Soc. Am. A: 11 2990Google Scholar

    [15]

    Wolff L B, Boult T E 1991 IEEE Trans. Pattern Anal. Mach. Intell. 13 635Google Scholar

    [16]

    Nayar S K, Fang X S, Boult T 1997 Int. J. Comput. Vision 21 163Google Scholar

    [17]

    Umeyama S J, Godin G 2004 IEEE Trans. Pattern Anal. Mach. Intell. 26 639Google Scholar

    [18]

    Atkinson G A, Hancock E R 2007 IEEE Trans. Pattern Anal. Mach. Intell. 29 2001Google Scholar

    [19]

    王文波, 张晓东, 汪祥莉 2013 物理学报 62 050201Google Scholar

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 050201Google Scholar

    [20]

    Comon P 1994 Signal Process. 36 287Google Scholar

    [21]

    TichavskÝ P, Kybernetika Z 2011 Kybernetika 47 426

    [22]

    Hyvarinen A, Oja E 2000 Neural Networks 13 411Google Scholar

    [23]

    Adachi K, Trendafilov N T 2018 Psychometrika 83 407Google Scholar

    [24]

    Li Z, Liu D T, Ye T, Chen G H, Zhang L H, Yang Q S, Ji F 2007 Chin. Phys. Lett. 16 2913Google Scholar

    [25]

    Biglieri E, Yao K 1989 Signal Process. 18 277Google Scholar

    [26]

    赵辽英, 吕步云, 厉小润, 陈淑涵 2015 物理学报 64 124204Google Scholar

    Zhao L Y, Lü B Y, Li X R, Chen S H 2015 Acta Phys.Sin. 64 124204Google Scholar

  • 图 1  物体表面反射光分类

    Fig. 1.  Classification of reflected light on the reflection surface.

    图 2  (a) 仿真人脸深度信息; (b), (c) 通过渲染后的漫反射和镜面反射仿真图像

    Fig. 2.  (a) Simulated face depth information; (b), (c) simulated images with diffuse and specular reflections after rendering, respectively.

    图 3  基于二色反射模型的四幅模拟图像 (a), (b), (c), (d)分别代表(8)式混合后的光强分布$I_1^{\rm{Total}}$, $I_2^{\rm{Total}}$, $I_3^{\rm{Total}}$$I_4^{\rm{Total}}$

    Fig. 3.  Four simulated images based on dichromatic reflection model, (a), (b), (c) and (d) represent the intensity image $I_1^{\rm{Total}}$, $I_1^{\rm{Total}}$, $I_3^{\rm{Total}}$ and $I_4^{\rm{Total}}$ obtained from Eq. (8), respectively.

    图 4  互信息随β变化情况及镜面反射和漫反射最终分离结果 (a) 分离的最优漫反射; (b) 分离的最优镜面反射

    Fig. 4.  Mutual information variation and the finally separated diffuse and specular reflection: (a) The optimal separated diffuse reflection; (b) the optimal separated specular reflection.

    图 5  10组仿真测试数据图 Coef1, Coef2, Coef3和Coef4是镜面反射分量混合系数的4个设定值; Coef1-Ours, Coef2-Ours, Coef3-Ours和Coef4-Ours是算法得到的混合系数值; Eu-Dis表示每组设定值组成的向量跟算法计算值组成的向量之间的欧氏距离

    Fig. 5.  Plots of ten sets of simulation test data: Coef1, Coef2, Coef3 and Coef4 are four set values of the coefficients of the specular reflection components; Coef1-Ours, Coef2-Ours, Coef3-Ours and Coef4-Ours are the coefficients obtained by our algorithm; Eu-Dis represents the Euclidean distance between the vector composed of set values and the vector composed of the calculated values of our algorithm in each group.

    图 6  陶瓷目标表面反射光强分布及分离结果 (a), (b)和(c)分别为图(d), (e)和(f)沿图示红虚线处的光强梯度分布; (d)是偏振片在0°方向下获取的目标原始光强图片; (e)和(f)分别是算法处理后最优的漫反射和镜面反射光强分布; (g)是算法处理过程中得到的互信息图; (h)是(d), (e), (f)沿图示蓝实线位置的光强截面结果

    Fig. 6.  Intensity distribution and separation results on the surface of ceramic object: (a), (b) and (c) are the light intensity gradient distribution along the red dotted line shown in Fig. (d), (e) and (f), respectively; (d) is the original light intensity of the target obtained from the polarizer at the direction of 0°; (e) and (f) are the optimal light intensity distributions of diffuse and specular component after our algorithm, respectively; (g) is the mutual information plot obtained during algorithm processing; (h) is the results of light intensity cross section obtained along the blue solid line in Fig. (d), (e) and (f).

    图 7  其他目标反射成分分离结果 (a) 总光强分布; (b), (c) 分离后的漫反射分量和镜面反射分量光强分布; (d), (e), (f) 分别为(a), (b), (c)的光强空间显示结果

    Fig. 7.  Reflection separation of three different objects: (a) Total light intensity distribution; (b), (c) light intensity distributions of separated diffuse and specular reflection, respectively; (d), (e), (f) spatial display of (a), (b) and (c) respectively.

  • [1]

    Zhang W Z, Chen Z B, Xia B F, Lin B, Cao X Q 2014 Chin. Phys. B 23 044212Google Scholar

    [2]

    Salvi J, Pages J, Batlle J 2004 Pattern Recognit. 37 827Google Scholar

    [3]

    Varady T, Martin R R, Cox J 1997 Comput.-Aided Des. 29 255Google Scholar

    [4]

    周光照, 王玉丹, 任玉琦, 陈灿, 叶琳琳, 肖体乔 2012 物理学报 61 018701Google Scholar

    Zhou G Z, Wang Y D, Ren Y Q, Chen C, Ye L L, Xiao T Q 2012 Acta Phys. Sin. 61 018701Google Scholar

    [5]

    Zhang R, Tsai P S, Cryer J E, Shah M 1999 IEEE Trans. Pattern Anal. Mach. Intell. 21 690Google Scholar

    [6]

    Jiang L, Zhang J Y, Deng B L, Li H, Liu L G 2018 IEEE Trans. Image Process. 27 4756Google Scholar

    [7]

    Shafer S A 1985 Color Res. Appl. 10 210Google Scholar

    [8]

    Klinker G J, Shafer S A, Kanade T 1988 Int. J. Comput. Vision 2 7Google Scholar

    [9]

    Klinker G J, Shafer S A, Kanade T 1990 Int. J. Comput. Vision 4 7Google Scholar

    [10]

    Novak C L, Shafer S A 1992 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Champaign, USA, June 15–18, 1992 p599

    [11]

    Tan P, Quan L, Lin S 2006 Proceedings 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition New York, USA, June 17–22, 2006 p1855

    [12]

    Shen H L, Zheng Z H 2013 Appl. Opt. 52 4483Google Scholar

    [13]

    Ren W H, Tian J D, Tang Y D 2017 IEEE Trans. Image Process. 26 2327Google Scholar

    [14]

    Sato Y, Ikeuchi K 1994 J. Opt. Soc. Am. A: 11 2990Google Scholar

    [15]

    Wolff L B, Boult T E 1991 IEEE Trans. Pattern Anal. Mach. Intell. 13 635Google Scholar

    [16]

    Nayar S K, Fang X S, Boult T 1997 Int. J. Comput. Vision 21 163Google Scholar

    [17]

    Umeyama S J, Godin G 2004 IEEE Trans. Pattern Anal. Mach. Intell. 26 639Google Scholar

    [18]

    Atkinson G A, Hancock E R 2007 IEEE Trans. Pattern Anal. Mach. Intell. 29 2001Google Scholar

    [19]

    王文波, 张晓东, 汪祥莉 2013 物理学报 62 050201Google Scholar

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 050201Google Scholar

    [20]

    Comon P 1994 Signal Process. 36 287Google Scholar

    [21]

    TichavskÝ P, Kybernetika Z 2011 Kybernetika 47 426

    [22]

    Hyvarinen A, Oja E 2000 Neural Networks 13 411Google Scholar

    [23]

    Adachi K, Trendafilov N T 2018 Psychometrika 83 407Google Scholar

    [24]

    Li Z, Liu D T, Ye T, Chen G H, Zhang L H, Yang Q S, Ji F 2007 Chin. Phys. Lett. 16 2913Google Scholar

    [25]

    Biglieri E, Yao K 1989 Signal Process. 18 277Google Scholar

    [26]

    赵辽英, 吕步云, 厉小润, 陈淑涵 2015 物理学报 64 124204Google Scholar

    Zhao L Y, Lü B Y, Li X R, Chen S H 2015 Acta Phys.Sin. 64 124204Google Scholar

  • [1] 张肃, 彭杰, 战俊彤, 付强, 段锦, 姜会林. 非球形椭球粒子参数变化对光偏振特性的影响. 物理学报, 2016, 65(6): 064205. doi: 10.7498/aps.65.064205
    [2] 李泽龙, 钟哲强, 张彬. 基于互补型偏振控制板的多光束叠加特性研究. 物理学报, 2014, 63(9): 095204. doi: 10.7498/aps.63.095204
    [3] 赵顾颢, 赵尚弘, 幺周石, 郝晨露, 蒙文, 王翔, 朱子行, 刘丰. 偏振无关的旋光双反射结构的实验研究. 物理学报, 2013, 62(13): 134201. doi: 10.7498/aps.62.134201
    [4] 马媛, 季小玲. 倾斜离轴高斯-谢尔模型光束在大气湍流中通过猫眼光学镜头反射光的光强特性. 物理学报, 2013, 62(9): 094214. doi: 10.7498/aps.62.094214
    [5] 张进, 周新星, 罗海陆, 文双春. 涡旋光束在反射中的正交偏振特性研究. 物理学报, 2013, 62(17): 174202. doi: 10.7498/aps.62.174202
    [6] 张小娟, 周青军, 杨薇. 光源附近空间分辨漫反射的SP3研究. 物理学报, 2012, 61(3): 034202. doi: 10.7498/aps.61.034202
    [7] 王锐, 王玉山. Delta-P1近似漫反射光学模型的二阶参量灵敏度. 物理学报, 2012, 61(18): 184202. doi: 10.7498/aps.61.184202
    [8] 田会娟, 牛萍娟. 基于混合漫射近似的空间分辨漫反射光学参量灵敏度的研究. 物理学报, 2012, 61(18): 184214. doi: 10.7498/aps.61.184214
    [9] 陈萍, 唐志列, 王娟, 付晓娣, 陈飞虎. 用Stokes参量法实现数字同轴偏振全息的研究. 物理学报, 2012, 61(10): 104202. doi: 10.7498/aps.61.104202
    [10] 刘迎, 刘小君, 齐贝贝, 田会娟. 生物组织的δ-P1近似漫反射光学模型. 物理学报, 2011, 60(7): 074204. doi: 10.7498/aps.60.074204
    [11] 付文羽, 马书懿. 部分相干平顶光束经光阑衍射的偏振特性. 物理学报, 2008, 57(2): 1271-1277. doi: 10.7498/aps.57.1271
    [12] 刘 迎, 王利军, 郭云峰, 张小娟, 高宗慧, 田会娟. 空间分辨漫反射的高阶参量灵敏度. 物理学报, 2007, 56(4): 2119-2123. doi: 10.7498/aps.56.2119
    [13] 刘廷禹, 张启仁, 庄松林. 钨酸铅晶体中与铅空位有关的电子结构和色心模型研究. 物理学报, 2006, 55(6): 2914-2921. doi: 10.7498/aps.55.2914
    [14] 郭少锋, 陆启生, 程湘爱, 周 萍, 邓少永, 银 燕. 反射光中Stokes成分对受激布里渊散射过程的影响. 物理学报, 2004, 53(6): 1831-1835. doi: 10.7498/aps.53.1831
    [15] 王 琛, 袁景和, 王桂英, 徐至展. 入射光的偏振特性对全内反射荧光显微术中荧光激发的影响. 物理学报, 2003, 52(12): 3014-3019. doi: 10.7498/aps.52.3014
    [16] 缪中林, 陈平平, 陆卫, 徐文兰, 李志锋, 蔡玮颖. GaAs/Al1-xAs表面单量子阱原位光调制反射光谱研究. 物理学报, 2001, 50(1): 111-115. doi: 10.7498/aps.50.111
    [17] 赵立竹, 申猛燕, 後藤武生. 气相法生长N-salicylideneaniline单晶及其偏振特性. 物理学报, 2001, 50(8): 1540-1544. doi: 10.7498/aps.50.1540
    [18] 游铭长, 张舒安. 海面漫反射率近似表示式的解析推导. 物理学报, 1994, 43(4): 683-688. doi: 10.7498/aps.43.683
    [19] 池坚刚, 赵文琴, 李爱珍. MBE GaAs1-xSbx/GaAs应变层量子阱的光调制反射光谱. 物理学报, 1989, 38(10): 1710-1716. doi: 10.7498/aps.38.1710
    [20] 方励之. 金属表面反射光中的谐波. 物理学报, 1964, 20(8): 817-818. doi: 10.7498/aps.20.817
计量
  • 文章访问数:  5793
  • PDF下载量:  185
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-07-04
  • 修回日期:  2020-07-24
  • 上网日期:  2020-11-30
  • 刊出日期:  2020-12-05

/

返回文章
返回