Optical elements defect online detection method based on camera array
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摘要:
利用调焦方式可以实现焦距的连续变化从而对不同物距下的光学组件进行在线检测，但是调焦过程操作复杂且对调焦位移精度要求较高，景深内光学元件缺陷无法区分，难以实现真正意义上的在线检测。因此，本文提出了基于相机阵列的光学组件缺陷在线检测方法。首先建立了相机阵列的成像模型并给出了数字重聚焦表达式以及空间分辨率的表达式。接着利用MATLAB模拟相机阵列成像过程和数字重聚焦过程。最后进行实验验证，通过二维位移台带动相机对不同物距下的多个光学元件表面缺陷进行成像获得阵列相机图像，通过数字重聚焦算法得到不同物距下的光学元件表面缺陷分布信息。实验结果表明，基于相机阵列的光学组件缺陷在线检测技术能够同时对位于景深范围内的光学组件进行在线检测。该方法在光学元件缺陷在线检测方面有着一定的应用价值。
Abstract:The focusing method can be used to achieve continuous change of the focal length, so that the optical components at different object distances can be detected online. However, the focusing process is complicated and requires high accuracy of the focusing displacement. Defects of optical components within the depth of field cannot be distinguished, making it difficult to achieve real online detection. Therefore, this paper proposes an online detection method for optical component defects based on camera arrays. First, the imaging model of the camera array is established, and the expressions of digital refocusing and the expressions of spatial resolution are given. Then MATLAB is used to simulate the camera array imaging process and digital refocusing process. Finally, experimental verification was conducted. The camera was driven by a two-dimensional stage to image the surface defects of multiple optical elements at different object distances, and an array camera image was obtained. The digital defect refocusing algorithm was used to obtain the surface defect distribution of optical elements at different object distances information. The experimental results show that the online detection technology of optical component defects based on camera array can simultaneously detect the optical components located in the depth of field range. This method has certain application value in the online detection of optical component defects.
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Key words:
- online detection /
- camera array /
- digital refocusing /
- optical components
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图 1 同一物体在相机阵列中分别成像示意图
Figure 1. Schematic diagram of the same object imaging separately in the camera array
图 4 重聚焦不同物距处的光学元件表面缺陷结果
Figure 4. Digital refocusing results of surface defects of optical elements at different object distances
图 5 光学组件缺陷检测结果示意图
Figure 5. Schematic diagram of defect detection results of optical components
图 6 基于相机阵列的光学组件缺陷检测示意图
Figure 6. Schematic diagram of optical components defect detection based on camera array
图 8 数字重聚焦和离线检测缺陷点外接矩形长宽对比示意图
Figure 8. Schematic diagram of length and width comparison of defects’ external rectangle under digital refocusing and off-line detection methods
表 1 不同数目的相机阵列对于目标3重聚焦图像的像质评价指标
Table 1. Image quality evaluation index of different number of camera arrays for target 3
camera array PSNR MSE 2×2 36.93 13.26 3×3 36.90 11.14 4×4 38.06 10.16 5×5 38.07 10.03 6×6 38.42 9.36 7×7 38.55 9.02 8×8 38.65 8.85 
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[1] 尤科伟, 张艳丽, 张雪洁, 等. 光学元件表面缺陷相对位置分布对近场光束质量的影响[J]. 中国激光, 2015, 42:0308004. (You Kewei, Zhang Yanli, Zhang Xuejie, et al. Influence of relative position distribution of surface defects on optical elements on near-field beam quality[J]. China Laser, 2015, 42: 0308004 doi: 10.3788/CJL201542.0308004 [2] 田爱玲, 王辉, 王春慧. 光学元件亚表面损伤的激光散射仿真研究[J]. 中国激光, 2013, 40:0908006. (Tian Ailing, Wang Hui, Wang Chunhui. Laser scattering simulation of subsurface damage of optical components[J]. China Laser, 2013, 40: 0908006 doi: 10.3788/CJL201340.0908006 [3] 杨甬英, 陆春华, 梁蛟, 等. 光学元件表面缺陷的显微散射暗场成像及数字化评价系统[J]. 光学学报, 2007, 27(6):1032-1038. (Yang Yongying, Lu Chunhua, Liang Jiao, et al. Microscattering dark field imaging and digital evaluation system for surface defects of optical elements[J]. Chinese Journal of Optics, 2007, 27(6): 1032-1038 [4] Thompson C E L. Optics damage inspection for the NIF[C]//Proc of SPIE. 1999, 3492:921-932. [5] Conder A, Alger T, Azevedo S, et al. Final optics damage inspection (FODI) for the National Ignition Facility[C]//Proc of SPIE. 2008:672010. [6] 邓燕, 许乔, 柴立群, 等. 光学元件亚表面缺陷的全内反射显微检测[J]. 强激光与粒子束, 2009, 21(6):836-839. (Deng Yan, Xu Qiao, Chai Liqun, et al. Total internal reflection microscopy of subsurface defects of optical elements[J]. High Power Laser and Particle Beams, 2009, 21(6): 836-839 [7] 赵文川, 钟显云, 刘彬. 基于条纹反射的光学表面疵病检测法[J]. 光子学报, 2014, 43:0912007. (Zhao Wenchuan, Zhong Xianyun, Liu Bin. Detection method of optical surface defects based on fringe reflection[J]. Journal of Photonics, 2014, 43: 0912007 [8] 任冰强, 黄惠杰, 张维新, 等. 光学元件损伤在线检测装置及实验研究[J]. 强激光与粒子束, 2004, 16(4):466-468. (Ren Bingqiang, Huang Huijie, Zhang Weixin, et al. On-line detection device and experimental study of optical element damage[J]. High Power Laser and Particle Beams, 2004, 16(4): 466-468 [9] 解亚平. 高功率固体激光光学元件损伤在线检测装置的研究[D]. 武汉: 华中科技大学, 2006: 35-40.Xie Yaping. Research on on-line damage detection device for high power solid laser optical elements[D]. Wuhan: Huazhong University of Science and Technology, 2006: 35-40 [10] 范哲源, 曹剑中, 屈恩世, 等. 一种8倍可见光变焦光学系统设计[J]. 光子学报, 2010, 39(s1):101-104. (Fan Zheyuan, Cao Jianzhong, Qu Enshi, et al. Design of an 8× visible light zoom optical system[J]. Journal of Photonics, 2010, 39(s1): 101-104 [11] 彭志涛. 强激光复杂光机组件光学元件激光损伤在线检测技术研究[D]. 绵阳: 中国工程物理研究院, 2011: 41-48.Peng Zhitao. On-line laser damage detection technology for optical components of high-power complex optical-mechanical components[D]. Mianyang: China Academy of Engineering Physics, 2011: 41-48 [12] 解亚平, 孙志红, 成泽, 等. 光学元件损伤在线检测中的图像处理[J]. 强激光与粒子束, 2006, 18(7):1086-1088. (Xie Yaping, Sun Zhihong, Cheng Ze, et al. Image processing in the online detection of optical element damage[J]. High Power Laser and Particle Beams, 2006, 18(7): 1086-1088 [13] 冯博, 刘炳国, 陈凤东, 等. ICF终端光学元件损伤在线检测装置的研究[J]. 红外与激光工程, 2013, 42(9):2519-2524. (Feng Bo, Liu Bingguo, Chen Fengdong, et al. On-line detection device for damage of optical components of ICF terminal[J]. Infrared and Laser Engineering, 2013, 42(9): 2519-2524 doi: 10.3969/j.issn.1007-2276.2013.09.040 [14] 董卫斌, 张敏, 达争尚, 等. 强激光装置中玻璃疵病在线检测的光学系统设计[J]. 光子学报, 2009, 38(3):685-688. (Dong Weibin, Zhang Min, Da Zhengshang, et al. Design of optical system for online detection of glass defects in high power laser device[J]. Journal of Photonics, 2009, 38(3): 685-688 [15] 肖照林. 基于相机阵列的光场成像与深度估计方法研究[D]. 西安: 西北工业大学, 2014: 35-40.Xiao Zhaolin. Research on camera-based light field imaging and depth estimation method[D]. Xi'an: Northwestern Polytechnical University, 2014: 35-40 [16] Zelnik-Manor L, Irani M. Multi-view subspace constraints on homographies[C]//International Conference on Computer Vision. 1999. [17] Hartley R, Zisserman A. Multiple view geometry in computer vision[M]. Cambridge: Cambridge University Press, 2003. [18] Zhang Z. A flexible new technique for camera calibration[J]. IEEE Trans Pattern Analysis and Machine Intelligence(TPAMI), 2000, 22(11): 1330-1339. doi: 10.1109/34.888718 -
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