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基于稀疏矩阵的光学元件表面疵病检测

Surface Defect Detection of Optical Components Based on Sparse Matrix

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摘要

提出了一种基于稀疏矩阵的表面疵病快速拼接方法。该方法采用环形白光光源均匀地照射到被测元件表面, 光经显微散射暗场成像系统后形成暗背景下的亮疵病图像。通过对光学元件的x, y方向进行扫描, 得到子孔径拼接图像。基于稀疏矩阵和图像拼接, 对子孔径图像进行快速拼接, 得到全孔径疵病图像。基于最小外接矩形原理, 对图像疵病进行识别和分类, 最终得到7个光学元件表面疵病划痕, 其最大长、宽分别为15.2110 mm和0.0297 mm; 麻点有5个, 其最大长、宽分别为0.1089 mm和0.0967 mm。将测量得到的划痕宽度与标准划痕宽度进行对比, 得到划痕宽度的相对误差范围为-5.00%~5.50%。在此基础上, 对实际的光学表面进行检测, 得到光学元件表面疵病信息。

Abstract

A fast mosaic method of surface defects based on sparse matrix is proposed. In the method, a ring white light source is used to irradiate uniformly on the component surface under test, and thus a bright defect image is formed in a dark background by a microscopic scattering dark field imaging system. Then the sub-aperture stitching image is obtained by scanning the x and y directions of optical elements. Based on sparse matrix and image stitching, the fast stitching of sub-aperture images is carried out to obtain a full-aperture defect image. Based on the principle of minimal external rectangle, the image defects are identified and classified. Seven defects on the surface of optical components are finally obtained, in which the largest one has a length of 15.2110 mm and a width of 0.0297 mm. Simultaneously there exist five pock marks, in which the largest one has a length of 0.1089 mm and a width of 0.0967 mm. The relative error range of scratch width is -5.00%-5.50% after comparison between the measured and standard scratch widths. On this basis, the actual optical surface is detected, and the surface defect information of optical elements is obtained.

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中图分类号:TH741

DOI:10.3788/cjl201946.0404007

所属栏目:测量与计量

基金项目:总装备部项目(JCKY2016208A001)、科技部国际合作项目(2015DFA10360)、激光与物质相互作用国家重点实验室基金(SKLLIM1703)

收稿日期:2018-12-04

修改稿日期:2018-12-20

网络出版日期:2019-01-04

作者单位    点击查看

陈晨:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
王红军:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
王大森:中国兵器科学院宁波分院, 浙江 宁波 310022
田爱玲:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
刘丙才:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
朱学亮:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
刘卫国:西安工业大学光电工程学院陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021

联系人作者:王红军(whj0253@sina.com)

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引用该论文

Chen Chen,Wang Hongjun,Wang Dasen,Tian Ailing,Liu Bingcai,Zhu Xueliang,Liu Weiguo. Surface Defect Detection of Optical Components Based on Sparse Matrix[J]. Chinese Journal of Lasers, 2019, 46(4): 0404007

陈晨,王红军,王大森,田爱玲,刘丙才,朱学亮,刘卫国. 基于稀疏矩阵的光学元件表面疵病检测[J]. 中国激光, 2019, 46(4): 0404007

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