光场成像目标测距技术

张石磊; 崔宇; 邢慕增; 闫斌斌

doi:10.37188/CO.2020-0043

光场成像目标测距技术

doi: 10.37188/CO.2020-0043

cstr: 32171.14.CO.2020-0043

张石磊^{1, 3,},
崔宇²,
邢慕增³,
闫斌斌^3, ,

1.
中国人民解放军92941部队，辽宁葫芦岛 125001
2.
上海航天技术研究院，上海 201100
3.
西北工业大学航天学院，陕西西安 710072

基金项目: 国家自然科学基金委员会中国工程物理研究院NSAF联合基金资助（No. U1730135）

详细信息

作者简介:
张石磊（1982—），男，黑龙江哈尔滨人，硕士，工程师，2020年于西北工业大学获得工学硕士学位，2006年起长期在92941部队从事测控靶标总体工作。E-mail: zsl71382@163.com

闫斌斌（1981—），男，陕西西安人，副教授，2010年于西北工业大学导航、制导与控制专业获得博士学位，主要研究方向为飞行器制导控制和无人系统自主飞行。E-mail: yanbinbin@nwpu.edu.cn

通讯作者:
闫斌斌（1981—），男，陕西西安人，副教授，2010年于西北工业大学导航、制导与控制专业获得博士学位，主要研究方向为飞行器制导控制和无人系统自主飞行。E-mail：yanbinbin@nwpu.edu.cn

中图分类号: TP391
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出版历程
- 收稿日期: 2020-03-20
- 修回日期: 2020-04-24
- 网络出版日期: 2020-11-10
- 刊出日期: 2020-12-01

Light field imaging target ranging technology

ZHANG Shi-lei^{1, 3
,},
CUI Yu²,
XING Mu-zeng³,
YAN Bin-bin^{3
, ,}

1.
Chinese People's Liberation Army 92941, Huludao 125001, China
2.
Shanghai Institute of Aerospace Technology, Shanghai 201100, China
3.
School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China

Funds: Supported by the Joint Fund of the National Natural Science Commission and China Academy of Engineering physics (No. U1730135)

More Information

Corresponding author: yanbinbin@nwpu.edu.cn

摘要

摘要: 为了使现代制导律能够在图像制导中得以应用，提高图像制导的性能，针对图像制导难以获取目标距离信息的问题，提出基于光场成像的目标测距算法。该算法首先对光场数据进行解码和整定，从原始图像中提取出子孔径图像；其次，对两张子孔径图像进行双线性插值，以提高图像的空间分辨率；之后，选取两张子孔径图像进行标定以获取对应的内参数和外参数，并利用这些参数校正子孔径图像，使其共面且行对准；最后，采用半全局匹配方法进行图像匹配，获取目标的视差值，将视差进行三维转换即可得到目标距离。实验结果表明，改进前、后算法的平均测量误差分别为28.54 mm和14.96 mm，距离测量精度得到有效提高，能够在较为复杂的场景中有效提取目标距离信息，具有一定的理论和应用价值。
- 光场成像 /
- 子孔径图像 /
- 距离测量
Abstract: At present, it is difficult to obtain target distance information in image guidance. In order to apply modern guidance laws to image guidance technology and improve its performance, a target ranging algorithm using light field imaging is proposed. The algorithm decodes and tunes light field data to extract sub-aperture images from an original image. Bilinear interpolation is then performed on the two sub-aperture images to improve the image’s spatial resolution, and two sub-aperture images are selected as calibration data to obtain the corresponding internal and external parameters. The parameters are used to correct the sub-aperture images, which aligns them and makes them coplanar. Finally, a semi-global matching method is used to match the images to obtain the disparity value of the target. Then, 3D transformation of parallax can be used to get the target distance. The experimental results show that the average measurement errors of the algorithm are 28.54 mm and 14.96 mm, respectively, before and after improvement. This algorithm can effectively extract target distance information in complex scenes, which has value in theoretical and real-world applications.
- light field imaging /
- sub-aperture images /
- distance measurement

HTML全文

图 1 光场成像原理

Figure 1. Light field imaging principle

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图 2 原始光场图像

Figure 2. Original light field image

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图 3 子孔径图像

Figure 3. Sub-aperture images

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图 4 4个坐标系空间相对位置

Figure 4. Relative position of four coordinate systems

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图 5 极线校正示意图

Figure 5. Schematic diagram of epipolar correction

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图 6 图像匹配示意图

Figure 6. Schematic diagram of image matching

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图 7 视差求取过程

Figure 7. Parallax finding process

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图 8 三维转换示意图

Figure 8. Schematic diagram of 3D conversion

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图 9 双线性插值

Figure 9. Bilinear interpolation

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图 10 不同姿态的标定板图像

Figure 10. Images of calibration plate with different attitudes

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图 11 标定板姿态复现

Figure 11. Calibration plate posture reproduction

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图 12 两张子孔径图像的校正图像

Figure 12. Correcting results of two sub-aperture images

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图 13 小参数匹配结果

Figure 13. Small parameter matching results

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图 14 大参数匹配结果

Figure 14. Large parameter matching results

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图 15 插值后匹配结果

Figure 15. Matching results after interpolation

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图 16 半全局匹配结果

Figure 16. Semi-global matching results

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图 17 双重改进结果

Figure 17. Double improvement results

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图 18 不同物体目标的测量结果（从左到右依次为原始图像，初始算法结果及双重改进算法结果）

Figure 18. Test results of different objects (From left to right are original image, initial algorithm results and double improvement algorithm results)

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表 1 不同算法的测量结果

Table 1. Measurement results with different algorithms

	距离测量/mm	有效像素点
初始算法	488.57	8051
双线性插值算法	480.59	21363
半全局匹配算法	483.10	16659
双重改进算法	466.55	57441

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表 2 实验结果

Table 2. Experiment results

	真实距离/mm	距离测量/mm	有效像素点	时间/s
A-初始算法	400	442.83	17218	1.31
A-改进算法	400	431.14	111380	6.42
B-初始算法	400	372.50	3161	1.06
B-改进算法	400	392.54	28557	5.83
C-初始算法	450	465.28	4156	1.27
C-改进算法	450	443.72	14168	5.76

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参考文献(20)

[1]	姚秀娟, 彭晓乐, 张永科. 几种精确制导技术简述[J]. 激光与红外,2006,36(5):338-340. doi: 10.3969/j.issn.1001-5078.2006.05.002 YAO X J, PENG X L, ZHANG Y K. Brief descriptions of precision guidance technology[J]. Laser &Infrared, 2006, 36(5): 338-340. (in Chinese) doi: 10.3969/j.issn.1001-5078.2006.05.002
[2]	胡林亭, 李佩军, 姚志军. 提高外场重频激光光斑测量距离的研究[J]. 液晶与显示,2006,31(12):1137-1142. HU L T, LI P J, YAO ZH J. Improvement of the measuring distance of repetitive-frequency laser spot in field[J]. Chinese Journal of Liquid Crystals and Displays, 2006, 31(12): 1137-1142. (in Chinese)
[3]	黄继鹏, 王延杰, 孙宏海. 激光光斑位置精确测量系统[J]. 光学精密工程,2013,21(4):841-848. doi: 10.3788/OPE.20132104.0841 HUANG J P, WANG Y J, SUN H H. Precise position measuring system for laser spots[J]. Optics and Precision Engineering, 2013, 21(4): 841-848. (in Chinese) doi: 10.3788/OPE.20132104.0841
[4]	谢艳新. 基于LatLRR和PCNN的红外与可见光融合算法[J]. 液晶与显示,2019,34(4):423-429. doi: 10.3788/YJYXS20193404.0423 XIE Y X. Infrared and visible fusion algorithm based on latLRR and PCNN[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(4): 423-429. (in Chinese) doi: 10.3788/YJYXS20193404.0423
[5]	赵战民, 朱占龙, 王军芬. 改进的基于灰度级的模糊C均值图像分割算法[J]. 液晶与显示,2020,35(5):499-507. doi: 10.3788/YJYXS20203505.0499 ZHAO ZH M, ZHU ZH L, WANG J F. Improved fuzzy C-means algorithm based on gray-level for image segmentation[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(5): 499-507. (in Chinese) doi: 10.3788/YJYXS20203505.0499
[6]	冯维, 吴贵铭, 赵大兴, 等. 多图像融合Retinex用于弱光图像增强[J]. 光学精密工程,2020,28(3):736-744. doi: 10.3788/OPE.20202803.0736 FENG W, WU G M, ZHAO D X, et al. Multi images fusion Retinex for low light image enhancement[J]. Optics and Precision Engineering, 2020, 28(3): 736-744. (in Chinese) doi: 10.3788/OPE.20202803.0736
[7]	YANG J C, EVERETT M, BUEHLER C. A real-time distributed light field camera[C]. Proceedings of the 13th Eurographics Workshop on Rendering, ACM, 2002: 77-86.
[8]	NG R. Digital light field photography[D]. California: Stanford University, 2006: 38-50.
[9]	计吉焘, 翟雨生, 吴志鹏, 等. 基于周期性光栅结构的表面等离激元探测[J]. 光学精密工程,2020,28(3):526-534. doi: 10.3788/OPE.20202803.0526 JI J T, ZHAI Y SH, WU ZH P, et al. Detection of surface plasmons based on periodic grating structure[J]. Optics and Precision Engineering, 2020, 28(3): 526-534. (in Chinese) doi: 10.3788/OPE.20202803.0526
[10]	于洁, 李鹏涛, 王春华, 等. RGBW液晶显示中的像素极性排布方式解析[J]. 液晶与显示,2020,35(5):444-448. doi: 10.3788/YJYXS20203505.0444 YU J, LI P T, WANG CH H, et al. Pixel polarity arrangement analysis of RGBW LCD module[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(5): 444-448. (in Chinese) doi: 10.3788/YJYXS20203505.0444
[11]	王江南, 丁磊, 倪婷, 等. 基于微结构阵列基板的高效顶发射OLED器件[J]. 液晶与显示,2019,34(8):725-732. doi: 10.3788/YJYXS20193408.0725 WANG J N, DING L, NI T, et al. High-efficiency top-emitting OLEDs based on microstructure array substrate[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(8): 725-732. (in Chinese) doi: 10.3788/YJYXS20193408.0725
[12]	解培月, 杨建峰, 薛彬, 等. 基于矩阵变换的光场成像及重聚焦模型仿真[J]. 光子学报,2017,46(5):0510001. doi: 10.3788/gzxb20174605.0510001 XIE P Y, YANG J F, XUE B, et al. Simulation of light field imaging and refocusing models based on matrix transformation[J]. Acta Photonica Sinica, 2017, 46(5): 0510001. (in Chinese) doi: 10.3788/gzxb20174605.0510001
[13]	张春萍, 王庆. 光场相机成像模型及参数标定方法综述[J]. 中国激光,2016,43(6):0609004. doi: 10.3788/CJL201643.0609004 ZHANG CH P, WANG Q. Survey on imaging model and calibration of light field camera[J]. Chinese Journal of Lasers, 2016, 43(6): 0609004. (in Chinese) doi: 10.3788/CJL201643.0609004
[14]	LIN X, RIVENSON Y, YARDIMCI N T, et al. All-optical machine learning using diffractive deep neural networks[J]. Science, 2018, 361(6406): 1004-1008. doi: 10.1126/science.aat8084
[15]	YAN T, WU J M, ZHOU T K, et al. Fourier-space diffractive deep neural network[J]. Physical Review Letters, 2019, 123(2): 023901. doi: 10.1103/PhysRevLett.123.023901
[16]	SHIN C, JEON H G, YOON Y, et al.. EPINET: a fully-convolutional neural network using epipolar geometry for depth from light field images[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 4748-4757.
[17]	PENG J Y, XIONG ZH W, LIU D, et al.. Unsupervised depth estimation from light field using a convolutional neural network[C]. Proceedings of 2018 International Conference on 3D Vision, IEEE, 2018: 295-303.
[18]	ZHOU T H, TUCKER R, FLYNN J, et al. Stereo magnification: learning view synthesis using multiplane images[J]. ACM Transactions on Graphics, 2018, 37(4): 65.
[19]	YEUNG H W F, HOU J H, CHEN J, et al.. Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues[C]. Proceedings of the 15th European Conference on Computer Vision, Springer, 2018: 137-152.
[20]	ZHANG ZH Y. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334. doi: 10.1109/34.888718