基于多图像融合的MEMS显微三维形貌重构

针对微型机电系统(MEMS)的三维测量, 显微镜或光学轮廓干涉仪等传统方法存在显微测量精度低、设备成本高等问题, 且当结构含有较多断裂面时, 解包裹算法效果欠佳。本文提出一种基于多图像融合的MEMS显微三维测量方法。不同于多角度显微三维测量方法, 本研究首先利用单目显微镜, 通过单一轴向移动获取一系列测量目标深度信息的单一角度图像, 并利用去雾算法对图像进行预处理, 实现了去噪和有效信息提取的目的; 然后通过聚焦测度算法获取待测对象的深度信息; 最后利用数据处理软件进行三维拟合。基于上述原理, 本文以焦平面阵列(FPA)作为待测目标进行了测量实验。本文提出的三维测量方法和图像处理算法可获得更准确的FPA形貌, 可清晰显示反射面与支腿部分及反射面上的释放孔, 测得FPA 的支腿长度为110.6 μm, 每个反射面的像元尺寸为120.8 μm×70.8 μm, 与设计值基本吻合, 解决了断裂面难以测量的问题, 同时降低了微结构测量的难度和成本。单目显微镜单向移动的多图像融合测量技术对MEMS的三维形貌测量具有重要意义, 去雾算法在图像融合与三维测量的图像处理也有很好的应用价值。

Abstract

In order to realize the three-dimensional measurement of micro electro-mechanical systems (MEMS), it is usually necessary to use a microscope or an optical profilometer. However, the traditional method has problems such as low precision and high cost of detection equipment, and it cannot achieve good results for an MEMS structure with many fracture surfaces. A new method for 3D measurement and image processing was presented in this paper. Unlike the three-dimensional reconstruction measurement method of multi-angle microscopes, this study firstly used a monocular microscope to obtain a series of single point measurements of the target, thus acquiring the depth information through single axial images. Then, a defogging algorithm was adopted for image preprocessing, denoising, and achieving effective information extraction. Subsequently, the depth information of the object to be tested was obtained using a focus measure algorithm. Finally, 3D fitting was performed using the data processing software. Based on the above principle, the focal plane array (FPA) was taken as the target to be measured. The experimental results show that the proposed 3D reconstruction method and image processing algorithm can obtain a more accurate FPA morphology, and can clearly show the release hole reflecting surface and the leg part on the reflector. The leg length of the FPA is measured to be 110.6 μm, and the pixel size of each reflector is about 120.8 μm×70.8 μm, which is consistent with the design value. It solves the problem of measuring the fracture surface, and reduces the difficulty and cost of microstructure measurement. The multi-directional image-fusion measurement technology using a monocular microscope is of great significance for MEMS 3D profile measurement. The defogging algorithm has significant application value in image fusion and 3D measurement image processing.

参考文献

[1] 张爱华, 李金平, 林冬梅. 基于视觉测量的脉搏图像三维重构［J］. 数据采集与处理, 2012, 27(5): 570-575.

ZHANG A H, LI J P, LIN D M. 3D Reconstruction of pulse image based on visual measurement ［J］. Journal of Data Acquisition & Processing, 2012, 27(5): 570-575. (in Chinese)

[2] 郭俊锋, 刘鹏, 焦国华, 等. 三维测量工业内窥镜的双目光学系统［J］. 光学精密工程, 2014, 22(9): 2337-2344.

GUO J F, LIU P, JIAO G H, et al.. Binocular optical system of industrial endoscope for three-dimensional measurement ［J］. Opt. Precision Eng., 2014, 22(9): 2337-2344. (in Chinese)

[3] 薛婷, 阮维鹏, 张少杰. 基于激光扫描的密集泡状流三维重建与优化［J］. 光学精密工程, 2017, 25(3): 555-561.

XUE T, RUAN W P, ZHANG SH J. 3D reconstruction and optimization of dense bubbly flow based on laser scanning ［J］. Opt. Precision Eng., 2017, 25(3): 555-561. (in Chinese)

[4] 史金龙, 钱强, 庞林斌, 等. 大尺度钢板的三维测量和拼接［J］. 光学精密工程, 2014, 22(5): 1165-1170.

SHI J L, QIAN Q, PANG L B, et al.. Three-dimensional measurement and registration for large scale plates ［J］. Opt. Precision Eng., 2014, 22(5): 1165-1170. (in Chinese)

[5] 陈国波. 基于散焦显微图像的三维重构方法研究［D］. 杭州: 浙江工业大学, 2007.

CHEN G B. Reserch of Defouces Microscopy Image 3-D Surface Reconstruction ［D］. Hangzhou: Zhejiang University of Technology, 2007. (in Chinese)

[6] 董鹤. 基于序列二维ISAR图像的目标三维重构方法的研究［D］. 哈尔滨: 哈尔滨工业大学, 2011.

DONG H. Three-dimensional Reconstruction Technique Based on 2-dimensional ISAR Image Sequence ［D］. Harbin: Harbin Institute of Technology, 2011. (in Chinese)

[7] 乌秀春. 反求工程中基于图像灰度信息的三维曲面重构研究［D］. 大连: 大连理工大学, 2002.

WU X CH. Study on 3D Surface Reconstruction Based on 2D Gray Image in Reverse Engineering ［D］. Dalian: Dalian University of Technology, 2002. (in Chinese)

[8] 郭东明, 乌秀春, 王晓明, 等. 反求工程中基于图像灰度信息的三维曲面重构［J］. 机械工程学报, 2003, 39(1): 47-50.

GUO D M, WU X CH, WANG X M et al.. 3D surface reconstruction based on gray image in reverse engineering ［J］. Chinese Journal of Mechanical Engineering, 2003, 39(1): 47-50. (in Chinese)

[9] 毛小俊. 单目多视角显微三维重建关键技术研究［D］. 杭州: 浙江工业大学, 2015.

MAO X J. Multi-view 3D Reconstruction using Monocular Microscopic Images ［D］. Hangzhou: Zhejiang University of Technology, 2015. (in Chinese)

[10] 林陶. 基于平移和旋转的单目视觉三维重建［D］. 杭州: 浙江工业大学, 2009.

LIN T. 3D Reconstruction of Monocular Vision Based on Translation and Rotation ［D］. Hangzhou: Zhejiang University of Technology, 2009. (in Chinese)

[11] 王跃宗. SLM显微立体视觉量化和三维数据重构研究［D］. 大连: 大连理工大学, 2003.

WANG Y Z. Study on the Quantification of Micro Stereovision with a SLM and 3D Data Reconstruction ［D］. Dalian: Dalian University of Technology, 2003. (in Chinese)

[12] 吕渭萍. 多视点深度图像的配准方法研究与实现［D］. 成都: 电子科技大学, 2008.

L W P. Research and Implementation of Multi View Depth Image Registration ［D］. Chengdu: University of Electronic Science and Technology of China, 2008. (in Chinese)

[13] 乐静, 杨洁, 王建, 等. 基于多层序列图像的三维表面粗糙度检测［J］. 仪器仪表学报, 2010, 31(9): 2036-2041.

LE J, YANG J, WANG J, et al.. Three-dimensional surface roughness measurement based on sequence multi-images ［J］. Chinese Journal of Scientific Instrument, 2010, 31(9): 2036-2041. (in Chinese)

[14] 杨洁. 基于多层序列图像的三维粗糙度测量方法的研究［D］. 西安: 西安理工大学, 2010.

YANG J. Research on Method of 3D Surface Roughness Measuring Based on Multi-sequence Images ［D］. Xian: Xian University of Technology, 2010. (in Chinese)

[15] 张浩鹏, 王宗义. 基于灰度方差和边缘密度的车牌定位算法［J］. 仪器仪表学报, 2011, 32(5): 1095-1102.

ZHANG H P, WANG Z Y. Car license plate location algorithm based on intensity variance and edge density ［J］. Chinese Journal of Scientific Instrument, 2011, 32(5): 1095-1102. (in Chinese)

[16] 杨旭朗, 侯榆青, 陈燊, 等. 一种基于图像灰度和方差信息的图像分割方法［J］. 量子电子学报, 2010, 27(6): 677-682.

YANG X L, HOU Y Q, CHEN SH, et al.. An image segmenting method based on image gray and variance information ［J］. Chinese Journal of Quantum Electronics, 2010, 27(6): 677-682. (in Chinese)

[17] HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior ［C］. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009: 1956-1963.

[18] FU ZH ZH, YANG Y J, SHU CH, et al.. Improved single image dehazing using dark channel prior ［J］. Journal of Systems Engineering and Electronics, 2015, 26(5): 1070-1079.

[19] 李佳童, 章毓晋. 图像去雾算法的改进和主客观性能评价［J］. 光学精密工程, 2017, 25(3): 735-741.

LI J T, ZHANG Y J. Improvements of image haze removal algorithm and its subjective and objective performance evaluation ［J］. Opt. Precision Eng., 2017, 25(3): 735-741. (in Chinese)

[20] 肖泉, 丁兴号, 廖英豪. 一种有效保持边缘特征的散焦模糊图像复原方法［J］. 计算机科学, 2010, 37(7): 270-272.

XIAO Q, DING X H, LIAO Y H. Novel edge-preserving algorithm for defocus blurred image restoration ［J］. Computer Science, 2010, 37(7): 270-272. (in Chinese)

丁汛, 赵跃进, 丁玉奎. 基于多图像融合的MEMS显微三维形貌重构[J]. 光学精密工程, 2018, 26(5): 1275. DING Xun, ZHAO Yue-jin, DING Yu-kui. Three-dimensional microscopic reconstruction of MEMS based on multi image fusion[J]. Optics and Precision Engineering, 2018, 26(5): 1275.

基于多图像融合的MEMS显微三维形貌重构

关于本站 Cookie 的使用提示

全站搜索

基于多图像融合的MEMS显微三维形貌重构

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索