应用光学, 2016, 37 (4): 542, 网络出版: 2016-08-29   

阶跃型三维形貌信息小波去噪方法研究

Wavelet denoising method for step three-dimensional shape information
作者单位
北京理工大学 光电学院 精密光电测试仪器及技术北京市重点实验室, 北京100081
摘要
阶跃样品显微测量时, 样品三维形貌本身丰富的阶跃信息极易受到噪声高频信号的干扰, 如何在滤除噪声的同时保持三维形貌的阶跃特征, 实现对样品表面三维形貌信息的高精度测量是一个重要研究问题。利用小波函数良好的空间域和频率域的局部化特性, 针对阶跃型样品的特点选取Haar小波, 并采用一种基于模平方的阈值处理方法对三维形貌信息小波去噪方法进行研究。将该方法应用在本课题组研制的激光差动共焦显微镜扫描台阶样品得到的三维高度轮廓中, 去噪后测量样品高度与OLYMPUS共焦显微镜扫描结果相对比, 误差为0.146 8 nm, 满足三维形貌信息后续测量分析的要求, 证明了算法的有效性。
Abstract
When measuring step samples by using microscope, a wealth of step information of three-dimensional shape itself is highly vulnerable to be interfered by high-frequency noise. How to maintain the step characteristics of three-dimensional shape while filtering noise and achieve high-precision measurement of three-dimensional shape of the sample surface information is an important research question. We did the research on wavelet denoising method based on modulus square threshold method for step three-dimensional shape information by using good space-domain and frequency-domain localization properties of wavelet function. Haar wavelet was selected for the step characteristics of the sample. The method was applied in the three-dimensional height profile which was obtained through laser differential confocal microscope developed by our research group. The height measurement result of the sample after denoising is consistent with the scanning result of OLYMPUS confocal microscope. The deviation is 0.146 8 nm. It can satisfy the requirement of the follow-up measurement analysis of three-dimensional shape information and prove the effectiveness of the algorithm.
参考文献

[1] 朱健军, 钟渊, 刘泊. 表面三维形貌测量及其评定的研究[J]. 哈尔滨理工大学学报, 2009, 14(1): 43-46.

    Zhu Jianjun, Zhong Yuan, Liu Bo. Measurement and assessment evaluation of the three-dimensional surface topography[J]. Journal of Harbin University of Science and Technology,2009,14(1): 43-46.

[2] 姚晋丽, 王霞. 一种基于小波变换的显微图像去噪算法研究与实现[J]. 计算机与数字工程, 2008, 36(7): 21-23,26.

    Yao Jinli,Wang Xia. Microscope image denoising algorithm study and realization based on wavelet transform[J]. Computer & Digital Engineering, 2008,36(7): 21-23,26.

[3] 匡海鹏, 王德江, 张景国, 等. 基于中值预滤波的航空图像小波去噪算法研究[J]. 应用光学, 2010, 31(2): 221-224.

    Kuang Haipeng, Wang Dejiang, Zhang Jingguo,et al.Aerial image wavelet transformation denoising based on medium pre-filtering[J]. Journal of Applied Optics, 2010,31(2): 221-224.

[4] 常亮亮, 王广龙. 基于中值滤波和提升小波分析的图像去噪方法研究[J]. 应用光学, 2012, 33(5): 894-898.

    Chang Liangliang, Wang Guanglong. De-noising method for mixed noise based on median filter and lifting wavelet transform[J].Journal of Applied Optics, 2012,33(5): 894-898.

[5] 任志英, 高诚辉, 申丁, 等. 双树复小波稳健滤波在工程表面粗糙度评定中的应用[J]. 光学精密工程,2014,22(7): 1820-1827.

    Ren Zhiying, Gao Chenghui, Shen Ding, et al. Application of DT-CWT robust filtering to evaluation of engineering surface roughness[J]. Opt. Precision Eng., 2014,22(7): 1820-1827.

[6] 刘长平, 杜世昌, 奚立峰. 基于剪切波的三维零件表面滤波方法[J]. 机械设计与研究,2014(6): 70-76.

    Liu Changping,Du Shichang, Xi Lifeng. Shearlet-based filtering method for three-dimensional engineering surfaces[J].Machine Design and Research,2014(6): 70-76.

[7] Zhao Weiqian, Tan Juibin, Qiu Lirong. Bipolar absolute differential confocal approachto higher spatial resolution[J]. Optics Express, 2004, 12(21): 5013-5021.

[8] 刘大礼. 抗反射率实时激光差动共焦显微成像方法与技术研究[D].北京: 北京理工大学,2015.

    Liu Dali. Method and technology research on real-time laser differential confocal microscopy without sample reflectivity[D]. Beijing: Beijing Institute of Technology.2015.

[9] Zhao Weiqian, Tan Juibin, Qiu Lirong , et al. A new laser heterodyne confocal probefor ultraprecision measurement of discontinuous contours[J]. Mesurement Science and Technology, 2005, 16(2): 497-504.

[10] 郭俊杰, 邱丽荣, 王允, 等. 用于惯性约束聚变靶丸测量的激光差动共焦传感器[J].光学精密工程, 2013,21(3): 644-651.

    Guo Junjie, Qiu Lirong, Wang Yun, et al. Laser differential confocal sensor for ICF capsule measurement[J]. Opt.Precision Eng., 2013, 21(3): 644-651.

[11] 吴光文, 王昌明, 包建东, 等. 基于自适应阈值函数的小波阈值去噪方法[J]. 电子与信息学报,2014,36(6): 1340-1347.

    Wu Guangwen, Wang Changming, Bao Jiandong, et al. A wavelet threshold de-noising algorithm based on adaptive threshold function[J].Journal of Electronics & Information Technology, 2014,36(6): 1340-1347.

[12] 纪峰, 李翠, 常霞, 等. 基于改进阈值函数的自适应图像去噪方法[J]. 传感技术学报,2014(3): 351-354.

    Ji Feng, Li Cui, Chang Xia, et al. Adaptive image denoising based on the improved threshold function.[J]. Chinese Journal of Sensors and Actuators[J], 2014(3): 351-354.

[13] 江虹, 苏阳. 一种改进的小波阈值函数去噪方法[J]. 激光与红外,2016,46(1): 119-122.

    Jiang Hong, Su Yang. Denoising method based on improved wavelet threshold function[J]. Laser & Infrared,2016,46(1): 119-122.

[14] 李海东, 李青. 基于阈值法的小波去噪算法研究[J]. 计算机技术与发展,2009,19(7): 56-58.

    Li Haidong, Li Qing. Wavelet denoising based on technique of threshold[J]. Computer Technology and Development,2009,19(7): 56-58.

[15] 李金伦,崔少辉,汪明. 基于改进中值滤波和提升小波变换的阈值去噪方法研究[J]. 应用光学,2014,35(5): 817-822.

    Li Jinlun, Cui Shaohui, Wang Ming. Threshold de-noising method for mixed noise based on improved median filter and lifting wavelet transform[J]. Journal of Applied Optics, 2014,35(5): 817-822.

樊颖, 邱丽荣, 赵维谦, 王允. 阶跃型三维形貌信息小波去噪方法研究[J]. 应用光学, 2016, 37(4): 542. Fan Ying, Qiu Lirong, Zhao Weiqian, Wang Yun. Wavelet denoising method for step three-dimensional shape information[J]. Journal of Applied Optics, 2016, 37(4): 542.

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