Halo-based black point detection method of microchannel plate
-
摘要: 根据微通道板黑点检测原理与黑点光晕特征设计了一种检测方法,该方法利用圆提取技术实现荧光屏图像的半径和圆心提取,通过高斯拉普拉斯算子实现目标黑点粗检测。基于黑点在不同电压下外围呈现光晕的特征,利用对比度受限的直方图均衡化并结合阈值分割的光晕检测法实现黑点光晕的提取。测试结果表明,该方法可以将荧光屏划痕、灰尘与微通道板黑点有效区分,从而实现黑点的自动提取。Abstract: According to the black spot detection principle and black spot halo characteristics of the microchannel plate, a detection method was designed. The circle extraction technology was used to realize the radius and circle center extraction of the fluorescent screen image, and the gaussian Laplacian was used to realize the coarse detection of the target black spot. Based on the characteristics of black spot presenting halo in the periphery at different voltages, the black spot halo was extracted by using the histogram equalization with restricted contrast and combined with the threshold segmentation of the halo detection method. The test results show that the proposed method can effectively distinguish the fluorescent screen scratches, dust and black spots of microchannel plate, so as to realize the automatic extraction of black spots.
-
Key words:
- microchannel plate /
- visual performance /
- defect detection /
- halo detection /
- threshold segmentation
-
表 1 各边缘检测算子检测结果
Table 1 Detection results of each edge detection operator
检测方法 检测时间/s 圆心坐标 半径 Canny 1.324 (2 556,1 900) 1 651 Sobel 0.433 (2 556,1 902) 1 651 Prewitt 0.728 (2 556,1 902) 1 652 Roberts 0.688 (2 558,1 901) 1 651 表 2 三种方法粗检测结果
Table 2 Coarse detection results of three methods
序号 文献9方法 Canny检测 本文方法 时间/ s 数量 T/F 时间/ s 数量 T/F 时间/ s 数量 T/F 1 30.273 6 186 T 9.800 285 F 21.412 133 T 2 32.518 9 937 T 9.102 286 T 22.230 493 T 3 34.190 12 093 T 9.161 701 T 22.152 277 T 4 29.725 4 619 T 9.296 78 F 21.742 37 T 表 3 疵点检测结果表
Table 3 Detection results of defects
序号 疵病类型 半径/ μm 机器检测结果 人工检测结果 序号 疵病类型 半径/μm 机器检测结果 人工检测结果 1 黑点 96 小黑点 小黑点 7 正常 0 − − 2 正常 0 − − 8 黑点 160 大黑点 大黑点 3 黑点 84 小黑点 小黑点 9 黑点 125 小黑点 小黑点 4 黑点 91 小黑点 小黑点 10 黑点 96 小黑点 小黑点 5 黑点 79 小黑点 小黑点 11 黑点 136 小黑点 小黑点 6 正常 0 − − 12 黑点 124 小黑点 小黑点 -
[1] 张小东, 欧阳晓平, 何军章, 等. 微通道板的增益研究[J]. 核技术,2019,42(10):50-54. doi: 10.11889/j.0253-3219.2019.hjs.42.100403ZHANG Xiaodong, OUYANG Xiaoping, HE Junzhang, et al. The study on the gain of microchannel plate[J]. Nuclear Techniques,2019,42(10):50-54. doi: 10.11889/j.0253-3219.2019.hjs.42.100403 [2] 刘术林, 李翔, 邓广绪, 等. 低噪声、高增益微通道板的研制[J]. 应用光学,2006,27(6):552-557. doi: 10.3969/j.issn.1002-2082.2006.06.019LIU Shulin, LI Xiang, DENG Guangxu, et al. Development of low-noise, high-gain microchannel plate[J]. Journal of Applied Optics,2006,27(6):552-557. doi: 10.3969/j.issn.1002-2082.2006.06.019 [3] 宋娟, 赵宝升, 盛立志, 等. 基于MCP大面阵X射线探测器共享阳极的研究[J]. 光子学报,2014,43(8):43-48.SONG Juan, ZHAO Baosheng, SHENG Lizhi, et al. Research on shared anode used for the large area array MCP detector[J]. Acta Photonica Sinica,2014,43(8):43-48. [4] 刘永安, 赵菲菲, 胡慧君, 等. 采用金阴极的光子计数成像探测器的性能[J]. 光学学报,2011,31(1):225-230.LIU Yongan, ZHAO Feifei, HU Huijun, et al. Properties of photon counting imaging detector with Au photocathode[J]. Acta Optica Sinica,2011,31(1):225-230. [5] 冉建玲, 卢小龙, 马占文, 等. 基于微通道板的快中子像探测器转换器的模拟研究[J]. 核技术,2015,38(9):33-39. doi: 10.11889/j.0253-3219.2015.hjs.38.090401RAN Jianling, LU Xiaolong, MA Zhanwen, et al. Simulation study on the converter of fast neutron imaging detector based on micro-channel plates[J]. Nuclear Techniques,2015,38(9):33-39. doi: 10.11889/j.0253-3219.2015.hjs.38.090401 [6] 司曙光, 金睦淳, 王兴超, 等. “日盲”紫外微通道板型光电倍增管研究[J]. 红外技术,2020,42(8):722-728. doi: 10.3724/SP.J.7102614852SI Shuguang, JIN Muchun, WANG Xingchao, et al. “solar blind” ultraviolet microchannel plate photomultiplier[J]. Infrared Technology,2020,42(8):722-728. doi: 10.3724/SP.J.7102614852 [7] KAMALAPRIYA M, THILAGAVATHI V. Automatic detection of visual defects in image intensifiers[C]//2012 National Conference on Communications (NCC). Kharagpur, India. USA: IEEE, 2012: 1-5. [8] FU Rongguo, WEI Yifang, YANG Qi, et al. The analysis of the defects of the view field of the UV image intensifier[J]. SPIE,2017,10196:19-26. [9] WANG Luzi, TAN Shuai, QIAN Yunsheng, et al. Automatic evaluation of vertex structural defects on the anode surface of a low-light-level image intensifier based on proposed individual image processing strategies[J]. Applied Optics,2021,60(23):6888-6901. doi: 10.1364/AO.427353 [10] 吕侃徽, 张大兴. 基于自适应直方图均衡化耦合拉普拉斯变换的红外图像增强算法[J]. 光学技术,2021,47(6):747-753. doi: 10.13741/j.cnki.11-1879/o4.2021.06.018LYU Kanhui, ZHANG Daxing. Infrared image enhancement algorithm based on adaptive histogram equalization coupled with Laplace transform[J]. Optical Technique,2021,47(6):747-753. doi: 10.13741/j.cnki.11-1879/o4.2021.06.018 [11] 刘宇涵, 闫河, 陈早早, 等. 强噪声下自适应Canny算子边缘检测[J]. 光学 精密工程,2022,30(3):350-362.LIU Yuhan, YAN He, CHEN Zaozao, et al. Adaptive Canny operator edge detection under strong noise[J]. Optics and Precision Engineering,2022,30(3):350-362. [12] 纵宝宝, 李朝锋, 桑庆兵. 基于Log-Gabor滤波与显著图融合优化的3D显著性检测[J]. 激光与光电子学进展,2019,56(8):109-115.ZONG Baobao, LI Chaofeng, SANG Qingbing. 3D image saliency detection based on log-Gabor filtering and saliency map fusion optimization[J]. Laser & Optoelectronics Progress,2019,56(8):109-115. [13] REISENHOFER R, KING E J. Edge, ridge, and blob detection with symmetric molecules[J]. SIAM Journal on Imaging Sciences,2019,12(4):1585-1626. doi: 10.1137/19M1240861 [14] 孙慧涛, 李木国. 多尺度光斑中心的快速检测[J]. 光学 精密工程,2017,25(5):1348-1356. doi: 10.3788/OPE.20172505.1348SUN Huitao, LI Muguo. Fast and accurate detection of multi-scale light spot centers[J]. Optics and Precision Engineering,2017,25(5):1348-1356. doi: 10.3788/OPE.20172505.1348 [15] YU Miao, SONG Dalong, SHI Weili, et al. Application of the CLAHE Algorithm Based on optimized bilinear interpolation in near infrared vein image enhancement[C]// Proceedings of the 2nd International Conference on Computer Science and Application Engineering, New York. United States: Association for Computing Machinery, 2018, 102: 1-6. [16] 袁小翠, 吴禄慎, 陈华伟. 基于Otsu方法的钢轨图像分割[J]. 光学 精密工程,2016,24(7):1772-1781. doi: 10.3788/OPE.20162407.1772YUAN Xiaocui, WU Lushen, CHEN Huawei. Rail image segmentation based on Otsu threshold method[J]. Optics and Precision Engineering,2016,24(7):1772-1781. doi: 10.3788/OPE.20162407.1772 [17] LIU Hao, LI Ce, ZHANG Dong, et al. Enhanced image no-reference quality assessment based on colour space distribution[J]. IET Image Processing,2020,14(5):807-817. doi: 10.1049/iet-ipr.2019.0856 -