期刊文献+

结合非连续性测度的方向偏微分方程在电子散斑干涉中的应用 被引量:3

The Oriented Partial Differential Equation Based on the Discontinuities Measure for Electronic Speckle Pattern Interferometry
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摘要 近年来,电子散斑干涉测量技术(ESPI)在光学粗糙表面的变形测量和无损检测方面应用广泛。应用该技术时,条纹图中大量的散斑噪声给提取条纹信息带来了极大的困难。偏微分方程图像滤波方法是一种方案灵活、处理效果良好的图像去噪方法,尤其是方向偏微分方程,因其只沿着条纹方向进行滤波,更适合电子散斑干涉条纹图。由于条纹图的疏密程度不同,因此在考虑滤波方向的同时,还应考虑不同位置像素点的滤波程度。该文在方向偏微分方程基础上引入非连续性测度,提出结合非连续性测度(DCM)的方向偏微分方程。利用该方程对模拟的条纹图以及实际获得的条纹图进行滤波,实验结果表明该文方法能够充分滤除稀疏条纹处的噪声,同时有效保持密集条纹处的重要特征。 The Electronic Speckle Pattern Interferometry (ESPI) is widely used for deformation measurement and nondestructive testing of the optical rough surface in recent years. Removal of the speckle noise is of fundamental importance for accurate extraction of the phase information. The Partial Differential Equation (PDE) filters are well-known for their good processing results, especially the oriented partial differential equation can control the direction of the filtering, which is more suitable for the ESPI image. Furthermore, the filtering degree of different pixels is considered. A new oriented PDE filter model is proposed for the ESPI fringe, in which the DisContinuities Measure (DCM) of an image is introduced to control the diffusion speed. The effectiveness of the proposed method is tested by means of the computer simulation and the experimentation on a real ESPI fringe patterns respectively. The results show that noise is effectively suppressed and the fringe edge is well preserved, even for very dense fringes.
出处 《电子与信息学报》 EI CSCD 北大核心 2014年第11期2600-2606,共7页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61102150) 天津市科技支撑计划重点项目(13ZCZDGX02100 14ZCZDGX00033)资助课题
关键词 图像处理 电子散斑干涉测量技术 偏微分方程 条纹滤波 非连续性测度 Image processing Electronic Speckle Pattern Interferometry (ESPI) Partial Differential Equation (PDE) Fringe filtering DisContinuities Measure (DCM)
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参考文献13

  • 1Tang C, Wang L, Yan H, et al.. Comparison on performance of some representative and recent filtering methods in electronic speckle pattern interferometry[J]. Optics and Lasers in Engineering, 2012, 50(8): 1036-1051.
  • 2王新强,张丽娟,班宝龙.基于滤波图像相减二值化的干涉条纹骨架线提取的研究[J].激光杂志,2012,33(6):28-29. 被引量:5
  • 3Yang X, Yu Q, and Fu S. A combined method for obtaining fringe orientations of ESPI[J]. Optics Communications, 2007, 273(1): 60-66.
  • 4Tang C, Han L, and Ren H. Second-order oriented partialdifferential equations for denoising in electronic-specklepattern interferometry fringes[J]. Optics Letters, 2008, 33(19): 2179-2181.
  • 5Perona P and Malik J. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
  • 6Saha P K and Udupa J K. Optimum image thresholding via class uncertainty and region homogeneity[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(7): 689-706.
  • 7迟华山,王红星,郭奇,赖淑蓉.短时傅里叶变换在线性调频信号时频滤波中的应用[J].电讯技术,2012,52(2):155-159. 被引量:18
  • 8Chikkerur S, Cartwright A N, and Govindaraju V. Fingerprint enhancement using STFT analysis[J]. Pattern Recognition, 2007, 40(1): 198-211.
  • 9Hong L, Wan Y, and Jain A. Fingerprint image enhancement: algorithm and performance evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 777-789.
  • 10刘西林,王泽文,邱淑芳.基于Priwitt算子的偏微分方程图像去噪模型[J].计算机应用,2012,32(12):3385-3388. 被引量:9

二级参考文献38

  • 1王智峰,李小毛,唐延东.一种改进的基于高斯曲率和偏微分方程的图像降噪算法[J].红外与激光工程,2006,35(z4):156-159. 被引量:3
  • 2伏思华,于起峰.数字散斑条纹图的滤波方法[J].应用光学,2005,26(4):5-8. 被引量:21
  • 3Gabor D. Theory of communication[ J]. Electrical Engineers, 1946, 93(26) :429 - 441.
  • 4Pinnegar C R,Eaton D W. Application of the S transform to prestack noise attenuation filtering[J]. Journal of Geophysical Research,2003,108(B9) :901 - 910.
  • 5RUDIN L, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[ J]. Physical D, 1992, 60(1/2/3/4) : 259 - 268.
  • 6YOU Y L , KAVEH M. Fourth-order partial differential equations for noise removal [ J]. IEEE Transactions on Image Processing, 2000, 9(10) : 1723 - 1730.
  • 7KIM S, LIM H. Fourth-order partial differential equations for effec- tive image denoising[ EB/OL]. [2012-05-20]. http://www, emis. de/joumals/EJDE/conf-proc/17/k2/kim, pdf.
  • 8YI D, LEE S. Fourth-order partial differential equations for image enhancement[ J]. Applied Mathematics and Computation, 2006, 175(1) :430 -440.
  • 9CHAN T, OSHER S, SHEN J. The digital TV filter and nonlinear denoising[J]. IEEE Transactions on Image Processing, 2001, 10 (2) : 231 -241.
  • 10ROMENY B. Geometry driven diffusion in computer vision[ M]. Berlin: Springer, 1994.

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