图像复原技术研究及应用
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摘要
图像复原是利用退化现象的某种先验知识(退化模型),按退化的逆过程重建图像的技术。噪声干扰和运动模糊是工程中常见的两种退化类型,本文研究了噪声滤波和运动模糊参数识别算法,以及这些算法在RFID倒装设备飞行视觉系统中的应用。
椒盐噪声严重降低了图像质量,其滤波性能的好坏直接影响后续图像处理的结果。本文提出了一个基于数学形态学的高污染椒盐噪声滤波算法。首先采用开、闭运算检测图像中的噪声并分类;然后针对检测到的椒噪声和盐噪声,设计相应的形态学滤波器;对滤波图像中的黑斑和白斑则采用一个简单的智能斑点擦除算法;最后加权求和形成开闭序列(OCS)滤波算法。仿真结果表明,与其他算法相比,该滤波算法能够有效地检测和滤除图像噪声,并保留更多的图像细节。
深入研究了图像匀速直线运动模糊的点扩展函数,提出其关键参数(模糊方向和模糊长度)识别的频域方法和微分方法。频域方法:通过分析模糊图像频谱图中的暗条纹特性,揭示了模糊方向与其垂直的关系,采用基于Radon变换的极大值(MRT)识别模糊方向;通过实验建立了模糊长度与暗条纹间距的反比例数学模型(IPM),并由运动方向上的Radon变换测定暗条纹间距。微分方法:采用双线性插值技术计算方向微分,模糊方向对应于图像最小方向微分(MDD);定义水平方向上微分图像的自相关,由微分自相关间距(PDA)计算模糊长度。研究了噪声对频域方法和微分方法的影响,并比较了两个方法的性能。实验表明,频域方法计算简单,结果准确,但有噪声污染时效果较差;微分方法不但计算准确,还具有一定的抗噪声能力。
设计了面向RFID装备的飞行视觉系统,所提出的OCS滤波算法、模糊参数识别的频域方法和微分方法都在其中得到应用。依据识别参数利用维纳滤波复原图像,并进行了模板匹配实验。应用实例表明,本文的图像复原技术可有效改善图像质量,提高匹配的准确度和精度。
Image restoration is an image reconstruction technology that image is rebuilt with some prior knowledge (degraded model) according to counter-process of degeneration. This dissertation involves the specific topics on the two models of degradation: noise jamming and motion blurring. The algorithms of noise removal and parameter identification for motion-blur are further studied and tested on the on-the-fly vision system of RFID flip chip bonder.
Impulse noise can seriously deteriorate image quality and the performance of its filter affects directly the result of subsequent image proceeding. A novel filter based on mathematical morphology for high probability impulse noise removal is presented. Firstly, an impulse noise detector using mathematical residues is proposed to identify pixels which are contaminated by the salt or pepper noise. Then the image is restored using specialized open-close sequence algorithms that apply only to the noise pixels. Finally, black and white blocks which degrade the quality of the image will be recovered by a smart block erase method. Experimental results demonstrate that the proposed filter outperforms a number of existing algorithms and can remove most of the noises effectively while preserving image details very well.
The point spread function for the image blur of uniform linear motion has been explored. Two methods based on frequency-domain and directional derivatives are proposed to identify two major blur parameters of the point spread function– blur direction and blur length. In the first method, the reason for the occurrence of black strips in the spectrum image is analyzed and the black strips that are perpendicular to the motion-blur direction are detected using radon transformation. The mathematical model of the relationship between blur length and the pitch of the dark lines is estimated based on the curve fitting algorithm. In the second method, the directional derivative is defined and the directional sub-pixel is calculated through bilinear interpolation. The angle corresponding to the minimum global directional derivatives is identified as blur direction. And the blur length is estimated by the minimal value of the derivative autocorrelation. Experimental results show that the frequency-domain method facilitates real-time calculation, while is not suitable for the noise image. The derivative method not only achieves accurate results, but also has strong noise immunity.
A novel on-the-fly vision system applied to RFID flip-chip bonder has been presented. The feasibility and availability of the proposed algorithms above have been tested and verified on RFID devices. Based on the identified parameters, the Wiener filtering is carried out to recover the motion-blurred image. And the restored images are further analyzed by pattern matching. Examples of application show that the proposed algorithms can efficiently enhance image quality and also improve the accuracy and precision of pattern matching.
椒盐噪声严重降低了图像质量,其滤波性能的好坏直接影响后续图像处理的结果。本文提出了一个基于数学形态学的高污染椒盐噪声滤波算法。首先采用开、闭运算检测图像中的噪声并分类;然后针对检测到的椒噪声和盐噪声,设计相应的形态学滤波器;对滤波图像中的黑斑和白斑则采用一个简单的智能斑点擦除算法;最后加权求和形成开闭序列(OCS)滤波算法。仿真结果表明,与其他算法相比,该滤波算法能够有效地检测和滤除图像噪声,并保留更多的图像细节。
深入研究了图像匀速直线运动模糊的点扩展函数,提出其关键参数(模糊方向和模糊长度)识别的频域方法和微分方法。频域方法:通过分析模糊图像频谱图中的暗条纹特性,揭示了模糊方向与其垂直的关系,采用基于Radon变换的极大值(MRT)识别模糊方向;通过实验建立了模糊长度与暗条纹间距的反比例数学模型(IPM),并由运动方向上的Radon变换测定暗条纹间距。微分方法:采用双线性插值技术计算方向微分,模糊方向对应于图像最小方向微分(MDD);定义水平方向上微分图像的自相关,由微分自相关间距(PDA)计算模糊长度。研究了噪声对频域方法和微分方法的影响,并比较了两个方法的性能。实验表明,频域方法计算简单,结果准确,但有噪声污染时效果较差;微分方法不但计算准确,还具有一定的抗噪声能力。
设计了面向RFID装备的飞行视觉系统,所提出的OCS滤波算法、模糊参数识别的频域方法和微分方法都在其中得到应用。依据识别参数利用维纳滤波复原图像,并进行了模板匹配实验。应用实例表明,本文的图像复原技术可有效改善图像质量,提高匹配的准确度和精度。
Image restoration is an image reconstruction technology that image is rebuilt with some prior knowledge (degraded model) according to counter-process of degeneration. This dissertation involves the specific topics on the two models of degradation: noise jamming and motion blurring. The algorithms of noise removal and parameter identification for motion-blur are further studied and tested on the on-the-fly vision system of RFID flip chip bonder.
Impulse noise can seriously deteriorate image quality and the performance of its filter affects directly the result of subsequent image proceeding. A novel filter based on mathematical morphology for high probability impulse noise removal is presented. Firstly, an impulse noise detector using mathematical residues is proposed to identify pixels which are contaminated by the salt or pepper noise. Then the image is restored using specialized open-close sequence algorithms that apply only to the noise pixels. Finally, black and white blocks which degrade the quality of the image will be recovered by a smart block erase method. Experimental results demonstrate that the proposed filter outperforms a number of existing algorithms and can remove most of the noises effectively while preserving image details very well.
The point spread function for the image blur of uniform linear motion has been explored. Two methods based on frequency-domain and directional derivatives are proposed to identify two major blur parameters of the point spread function– blur direction and blur length. In the first method, the reason for the occurrence of black strips in the spectrum image is analyzed and the black strips that are perpendicular to the motion-blur direction are detected using radon transformation. The mathematical model of the relationship between blur length and the pitch of the dark lines is estimated based on the curve fitting algorithm. In the second method, the directional derivative is defined and the directional sub-pixel is calculated through bilinear interpolation. The angle corresponding to the minimum global directional derivatives is identified as blur direction. And the blur length is estimated by the minimal value of the derivative autocorrelation. Experimental results show that the frequency-domain method facilitates real-time calculation, while is not suitable for the noise image. The derivative method not only achieves accurate results, but also has strong noise immunity.
A novel on-the-fly vision system applied to RFID flip-chip bonder has been presented. The feasibility and availability of the proposed algorithms above have been tested and verified on RFID devices. Based on the identified parameters, the Wiener filtering is carried out to recover the motion-blurred image. And the restored images are further analyzed by pattern matching. Examples of application show that the proposed algorithms can efficiently enhance image quality and also improve the accuracy and precision of pattern matching.
引文
[1]胡一凡.RFID射频识别技术综述.计算机时代[J].2006,12:3-4
[2]林苑晴.RFID设备的未来与挑战.电子技术[J].2005,3:70-71
[3]丁汉,朱利民,林忠钦.面向芯片封装的高加速度运动系统的精确定位和操作.自然科学进展[J],2003,13(6):568-574
[4]刘辉.RFID标签封装设备中机器视觉系统设计与实现.硕士学位论文,武汉:华中科技大学,2006
[5]陈建魁.面向RFID封装的柔性基板输送装置的研究与实现.硕士学位论文,武汉:华中科技大学,2006
[6] Gillbertt Learpentier, Jean-Stephane Mottetet, Jeffrey Dumas, et al.“High accuracy machine automated assembly for opto-electronics,”50th Electronic Components and Technology Conference. USA, Las Vegas: IEEE, 2000: 1-4,
[7] K. R. Castleman, Digital Image Processing, Prentice Hall, 1998
[8] M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis and Machine Vision, Second edition. Thomson Asia Pte Ltd, United States of America, 1998
[9]夏德深.现代图像处理技术与应用[M].江苏:东南大学出版社,1999:113
[10] Alex Rav-Acha, Shmuel Peleg,“Two motin-blurred images are better than one,”Pattern Recognition Letters, 2005, 26: 311-317,
[11] Ben-Ezra, M., Nayar, S. K.“Motion deblurring using hybrid imaging.”IEEE Conference on Computer Vision and Pattern Recognition. 2003: 454-460
[12]章毓晋.图像处理和分析[M].北京:清华大学出版社,1999
[13] J Astola, P Kuosmanen. Fundamentals of Nonlinear Digital Filtering. Boca Raton. FL: CRC, 1997
[14] L. Yin, R. Yang, M. Gabbouj, Y. Neuvo.“Weighted median filters: a tutorial,”IEEE Translations on Circuits and Systems, 1996, 43(3): 157-192
[15] S.J. Ko and Y.H. Lee,“Center weighted median filters and their applications to image enhancement,”IEEE Trans. On Circuits System, 1991, 38: 984 - 993
[16] T. Chen, H. R. Wu.“Space variant median filters for the restoration of impulse noise corrupted images,”IEEE Translations on Circuits and Systems-II: Analog and Digital Processing, 2001, 48(8): 784-789
[17] T Sun, Y Neuvo.“Detail-preserving median based filters in image processing,”PatternRecognit Letters, 1994, 15: 341-347
[18] D Florencio, R Schafer.“Decision-based median filter using local signal statistics,”Proc. SPIE Visual Communicationns Image Processing, Chicago, 1994
[19] V. Crnojevic, V. Senk, and Z. Trpovski,“Advanced impulse detection based on pixel-wise MAD”, IEEE Signal Processing Letters, 2004, 11(7): 589– 592
[20] H.-L. Eng and K.-K. Ma,“Noise adaptive soft-switching median filter,”IEEE Translations on Image Processing, 2001, 10: 242–251
[21]董继扬,张军英.一种简单的椒盐噪声滤波算法[J].计算机工程与应用,2003,20:27-28
[22] Zhou Wang, David Zhang,“Progressive switching median filter for the removal of impulse noise from highly corrupted image,”IEEE Trans. On Circuits and Systems-II: Analog and Digital Signal Processing , 1999, 46(1): 78-80
[23] S.Zhang and MA Karim, "A new impulse detector for switching median filters," IEEE Signal Processing Letters, 2002, 9(11): 360-363
[24] X. K. Xiao, S. F. Li,“Detail-preserving approach for impulse noise removal from images,”Proceedings of the Fourth International Conference on Computer and Information Technology, 1994: 28-32
[25] K. C. Lee, H. J. Song, KH.Sohn,“Detection-estimation based approach for impulsive noise removal,”Electronics Letters, 1998, 34(5): 449-450
[26] T. Chen and H. Wu,“Adaptive impulse detection using center-weighted median filters,”Signal Processing Letters, 2001, 8(1): 1–3
[27] T. Loupas, W. N. Mcdicken, et al.,“An adaptive weighted median filter for speckle suppression in medical ultrasonic images,”IEEE Translations on Circuits and Systems, 1984, 36(1): 129-135
[28] Akira Taguchi. A design method of fuzzy weighted median filters. IEEE International Conference on Image Processing, 1996, 1: 423-426
[29] Yang, X., Toh, PS,“Adaptive fuzzy multilevel median filter,”IEEE Translations on Image Processing, 1995, 4: 680-682
[30]张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波器[J].计算机辅助与图形学学报,2005,17(2):295-299
[31]张旭明,徐滨士等.自适应中值-加权均值混合滤波器[J].光学技术,2004,30(6):652-655
[32] S. M. Peng, L. Lucke,“A hybrid filter for image enhancement,”IEEE InternationalConference on Image Processing, 1995, 1: 163-166
[33] I. Pitas, A. N. Venetanopoulos, Nonlinear Digital Filter. Boston: Kluwer, 1989
[34] L. Yin, Y. Neuvo,“Fast adaptation and performance characteristics of FIR-WOS hybrid filters,”IEEE Trans. On Signal Processing, 1994, 7: 1610-1628
[35] Akira T, Mitsuji M, Takao H.“Median and neural networks hybrid filters,”IEEE International Conference on Neural Networks, 1995, 1: 580-583
[36] L. Yin, J. Astola, and Y. Neuvo,“A New Class of Nonlinear Filters-Neural Filters,”IEEE Trans. On Signal Processing, 1983, 3: 1201-1222
[37] P. Maragos, R.W. Schafer,“Morphological filters-part I: their set-theoretic analysis and relations to linear shift-invariant,”IEEE Trans. on Acoustics, Speech, and Signal Processing, 1987, 35(8): 1153-1169
[38] A.Kher and S.Mitra,“Optimum morphological filtering to remove speckle noise from SAR images, image algebra and morphological image processing IV,”Proc. SPIE, 1993, 2030: 97-108
[39] F. Safa and G.. Flouzat,“Speckle removal on radar imagery based on mathermatical morphology,”Signal Processing, 1989, 16(4): 319-333
[40] S. Tsekeridou, C. Kotropoulos, I. Pitas,“Morphological signal adaptive median filter for noise removal,”Proceedings of the Third IEEE International Conference on Electronics, Circuits, and Systems, 1996, 1: 191-194
[41] R. Bernstein,“Adaptive nonlinear filter for simultaneous removal of different kinds of noise in images,”IEEE Translations on Circuits and Systems, 1987, 11: 1275-1291
[42] M. H. Sedaaghi, R. Daj and M.Khosravi.“Mediated morphological filters,”International Conference on Image Processing, 2001: 692-695
[43] J. Oh and L. F. Chaparro,“Adaptive fuzzy morphological filtering of images,”Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1998, 5: 2901-2904
[44] J. Song and E. Delp,“The analysis of morphological filters with multiple structuring elements,”Computer Vision Graphics, Image Processing, 1990, 50: 308-328
[45] Mukhopadhysy S, Chanda B,“An edge preserving noise smoothing technique using multi-scale morphology,”Signal Processing, 2002, 82: 527-544
[46] E. Abreu, M. Lightstone, SK Mitra, and K. Arakawa,“A new efficient approach for the removal of impulse noise from highly corrupted images,”IEEE Translations on Image Process, 1996, 5(6): 1012-1025
[47] Han W, Lin W.“Minimum-maximum exclusive mean(MMEM) filter to removeimpulse noise from highly corrupted images,”Electronics Letters, 1997, 33(2): 124-125
[48] Minggang Ma, Xiao Jiao and Xinquan Tan.“Fuzzy hybrid filter for removal of impulse noise from highly corrupted images,”2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, 2002, 2: 885-888
[49] Mituhiro Okano, Akira Taguchi and Mototaka Sone.“Restoration method of images corrupted by high probability impulse noise by using fuzzy technique,”WCCC-ICSP 2000 5th International Conference on Signal Processing, 2000, 1: 244-247
[50] Shih-Mao Lu, Her-Chang Pu and Chin-Teng Lin.“A HVS-directed neural-network-based approach for impulse-noise removal from highly corrupted images,”IEEE International Conference on Systems, Man and Cybernetics, 2003, 1: 72-77
[51]董继扬,张军英.严重椒盐噪声污染图像的非线性滤波算法.光电子激光[J]. 2003,14(12):1336-1339
[52] XU X. Y., MILLER E. L.“Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images,”IEEE Translations on Image Processing, 2004, 13: 238 -247
[53] Sanjit K. Mitra, Tian-hu Yu.“A new nonlinear algorithm for the removal of impulse noise from highly corrupted images,”IEEE International Symposium on Circuits Systems, 1994, 3: 17-20
[54] Eduardo Abreu, Michael Lightstone, et al.“A new efficient approach for the removal of impulse noise from highly corrupted images,”IEEE Translations on Image Processing, 1996, 5: 1012-1024
[55] M. Ben-Ezra and S. K. Nayar,“Motion-based motion deblurring,”IEEE Translations on Pattern Analysis and Machine Intelligence, 2004, 26(6): 689-698
[56]阮秋琦.数字图像处理学[M].北京:电子工业出版社,2001
[57]高异,梁秀梅,于平义.车辆运动模糊图像的快速恢复[J].计算机工程与应用,2004,2:215-218
[58] YITZHAKY Y., KOPEIKA N. S.,“Identification of blur parameters from motion blurred images,”Graphical Models and Image Processing, 1997, 59(5): 310-320
[59] Yitzhaky Y, Boshusha G, Lem Y, et al.“Vibrated image restoration from a single frame.”SPIE, 1999, 3808: 603-613
[60] Yitzhaky Y, Mor I, Lantzman A, et al.“Direct method for restoration of motion-blurred images,”Journal of Optical Society of America, 1998, 15(6): 1512-1519
[61] Yitzhaky Y, Milberg R, Yohaev S, et al.“Comparison of direct blind deconvolution methods for motion-blurred image,”Applied Optics, 1999, 38(20): 4325-4332
[62] Kopeika-NS S-A.“General restoration filter for vibrated-image restoration,”Applied Optics, 1998, 37(32): 7596-7603
[63]陈前荣,陆启生,成礼智.基于方向微分和加权平均的运动模糊方向鉴别[J].计算机工程与应用,2004,29:1-5
[64]陈前荣,陆启生,成礼智.利用拉氏算子鉴别运动模糊方向[J].计算机应用, 2004,24(9):4-6
[65]王晓红,赵荣椿.匀速直线运动模糊的PSF之估计[J].计算机应用,2001, 21(9):40-41
[66] Cannon M.,“Blind deconvolution of spatially invariant image blurs with phase,”IEEE Transactions Acoustics Speech Signal Processing, 1976, 24(1): 58-63
[67]高梅,陈树越.任意方向运动模糊图像的恢复[J].电脑开发与应用, 2004, 17(7):12-13
[68] M. E. Moghaddam, M. Jamzad.“Finding point spread function of motion blur using radon transform and modeling the motion length,”Signal Processing and Information Technology, 2005: 862-866
[69] A.K.Katsaggelos, Ed., Digital Image Restoration, Springer-Verlag, NewYork, 1991
[70] R. L. Lagendijk, A. M. Tekalp, and J. Biemond,“Maximum likelihood image and blur identificaion: A unifying approach,”Optical Engineering, 1990, 29: 422-435
[71] G.Pavlovic and A. M. Tekalp,“Maximum likelihood parametric blur identification based on a continuous spatial domain model,”IEEE Translations on Image Processing, 1992, 1: 496-504
[72]鲜飞.贴片机视觉对中系统[J].电子工艺技术,2005,26(5):150-154
[73] Michael K.Bartschet, Joseph S.Kovalchick.“Method and apparatus for chip placement,”United States Patent: 4980971, 1991
[74]宋福民,张小丽,马如震.SMT2505全视觉多功能贴片机的研制[J].电子工业专用设备,2002,31(4):219-223
[75] A.Yamauchi, Y.Arai.“Analysis and measures against heat-expansion for sub-micron LD assembly by passive alignment,”51st Electronic Components and Technology Conference. USA, Orlando: IEEE, 2001: 242-246
[76] Uwe Tews, Maximilian R.Rottmann.“Method and an apparatus for the positioning ofcomponents with reference to a workpiece,”United States Patent: 4615093, 1986
[77] Edison T. Hudson.“Method and apparatus for reflective in-flight component registration,”United States Patent: 5768759, 1998
[78] Edison T. Hudson.“One camera system for component to substrate registration,”United States Patent: 20010055069, 2001
[79] Daniel Link.“Appratus for mounting a flipchip on a work piece,”United Stated Patent: 6530146, 2003
[80] Tukey J. Exploratory Data Analysis. Addison-wesley, Mass 1971: 236-281
[81] J. Serra. Image analysis and mathematical morphology. Academic Press, London, 1982
[82] J. Serra,“Morphological filtering: an over view,”Signal Processing, 1994, 38(1): 3-11
[83] Sternberg S. R., Grayscale morphology. Computer vision, Graphics, and Image Processing. 1986
[84] Image Processing Toolbox, Matlab Help
[85] Xinhua Zhuang,“Decomposition of morphological structuring elements,”Journal of Mathematical Imaging and Vision, 1994, 4(1): 5-18
[86] Jones R., Svalbe I.,“Basis decomposition of morphological operations,”Pattern Recognition, 1992, 3: 264-267
[87]孙兆林.MATLAB 6.x图像处理[M].北京:清华大学出版社,2002
[88]张秉仁,陈里铭,高游.基于图像运动的图像模糊机理与恢复技术[J].计算机工程. 2004,30(8):21-22
[89]李伟光,曾志军,曾志新,邹海明.表面组装装备机器视觉系统研究[J].组合机床与自动化加工技术,2005,9:66-67,70
[90]杨晓飞,韩昌元.利用离轴三反镜光学系统确定各镜的装调公差[J].光学技术,2005,31(2):173-176
[91] A. K. Katsaggelos, J.Biemond, R.M.Mersereau, R. WSchafer.“A general formulation of constrained iterative restoration algorithms,”IEEE International Conference on Acoustics, Speech, and Signal Processing, 1985, 10: 700-703
[92] Steven S. O. CHOY, Yuk-Hee CHAN, Wan-Chi SIM.“New adaptive iterative image restoration algorithm,”IEEE International Conference on Image Processing, 1994, 2: 670-674
[93] J. W. Woods and V. K. Ingle.“Kalman filtering in two-dimensions: further results,”IEEE Trans. Acoust. , Speech, Signal Processing. 1981, 29(2): 188-197
[94] N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MITPress, Cambridge, MA, 1942
[95] Euresys,几何特征模板匹配.http://www.euresys.com/CN/Products/EasyFindCn.asp
[96]邸慧,丁晓华,于起峰.基于Z变换匀速旋转运动模糊图像的快速恢复[J].光电工程,2006,33(4):89-92,110
[97]洪汉玉,张天序.非零边界旋转运动模糊图像的恢复算法[J].中国图象图形学报,2004,9(3):265-274
[2]林苑晴.RFID设备的未来与挑战.电子技术[J].2005,3:70-71
[3]丁汉,朱利民,林忠钦.面向芯片封装的高加速度运动系统的精确定位和操作.自然科学进展[J],2003,13(6):568-574
[4]刘辉.RFID标签封装设备中机器视觉系统设计与实现.硕士学位论文,武汉:华中科技大学,2006
[5]陈建魁.面向RFID封装的柔性基板输送装置的研究与实现.硕士学位论文,武汉:华中科技大学,2006
[6] Gillbertt Learpentier, Jean-Stephane Mottetet, Jeffrey Dumas, et al.“High accuracy machine automated assembly for opto-electronics,”50th Electronic Components and Technology Conference. USA, Las Vegas: IEEE, 2000: 1-4,
[7] K. R. Castleman, Digital Image Processing, Prentice Hall, 1998
[8] M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis and Machine Vision, Second edition. Thomson Asia Pte Ltd, United States of America, 1998
[9]夏德深.现代图像处理技术与应用[M].江苏:东南大学出版社,1999:113
[10] Alex Rav-Acha, Shmuel Peleg,“Two motin-blurred images are better than one,”Pattern Recognition Letters, 2005, 26: 311-317,
[11] Ben-Ezra, M., Nayar, S. K.“Motion deblurring using hybrid imaging.”IEEE Conference on Computer Vision and Pattern Recognition. 2003: 454-460
[12]章毓晋.图像处理和分析[M].北京:清华大学出版社,1999
[13] J Astola, P Kuosmanen. Fundamentals of Nonlinear Digital Filtering. Boca Raton. FL: CRC, 1997
[14] L. Yin, R. Yang, M. Gabbouj, Y. Neuvo.“Weighted median filters: a tutorial,”IEEE Translations on Circuits and Systems, 1996, 43(3): 157-192
[15] S.J. Ko and Y.H. Lee,“Center weighted median filters and their applications to image enhancement,”IEEE Trans. On Circuits System, 1991, 38: 984 - 993
[16] T. Chen, H. R. Wu.“Space variant median filters for the restoration of impulse noise corrupted images,”IEEE Translations on Circuits and Systems-II: Analog and Digital Processing, 2001, 48(8): 784-789
[17] T Sun, Y Neuvo.“Detail-preserving median based filters in image processing,”PatternRecognit Letters, 1994, 15: 341-347
[18] D Florencio, R Schafer.“Decision-based median filter using local signal statistics,”Proc. SPIE Visual Communicationns Image Processing, Chicago, 1994
[19] V. Crnojevic, V. Senk, and Z. Trpovski,“Advanced impulse detection based on pixel-wise MAD”, IEEE Signal Processing Letters, 2004, 11(7): 589– 592
[20] H.-L. Eng and K.-K. Ma,“Noise adaptive soft-switching median filter,”IEEE Translations on Image Processing, 2001, 10: 242–251
[21]董继扬,张军英.一种简单的椒盐噪声滤波算法[J].计算机工程与应用,2003,20:27-28
[22] Zhou Wang, David Zhang,“Progressive switching median filter for the removal of impulse noise from highly corrupted image,”IEEE Trans. On Circuits and Systems-II: Analog and Digital Signal Processing , 1999, 46(1): 78-80
[23] S.Zhang and MA Karim, "A new impulse detector for switching median filters," IEEE Signal Processing Letters, 2002, 9(11): 360-363
[24] X. K. Xiao, S. F. Li,“Detail-preserving approach for impulse noise removal from images,”Proceedings of the Fourth International Conference on Computer and Information Technology, 1994: 28-32
[25] K. C. Lee, H. J. Song, KH.Sohn,“Detection-estimation based approach for impulsive noise removal,”Electronics Letters, 1998, 34(5): 449-450
[26] T. Chen and H. Wu,“Adaptive impulse detection using center-weighted median filters,”Signal Processing Letters, 2001, 8(1): 1–3
[27] T. Loupas, W. N. Mcdicken, et al.,“An adaptive weighted median filter for speckle suppression in medical ultrasonic images,”IEEE Translations on Circuits and Systems, 1984, 36(1): 129-135
[28] Akira Taguchi. A design method of fuzzy weighted median filters. IEEE International Conference on Image Processing, 1996, 1: 423-426
[29] Yang, X., Toh, PS,“Adaptive fuzzy multilevel median filter,”IEEE Translations on Image Processing, 1995, 4: 680-682
[30]张旭明,徐滨士,董世运.用于图像处理的自适应中值滤波器[J].计算机辅助与图形学学报,2005,17(2):295-299
[31]张旭明,徐滨士等.自适应中值-加权均值混合滤波器[J].光学技术,2004,30(6):652-655
[32] S. M. Peng, L. Lucke,“A hybrid filter for image enhancement,”IEEE InternationalConference on Image Processing, 1995, 1: 163-166
[33] I. Pitas, A. N. Venetanopoulos, Nonlinear Digital Filter. Boston: Kluwer, 1989
[34] L. Yin, Y. Neuvo,“Fast adaptation and performance characteristics of FIR-WOS hybrid filters,”IEEE Trans. On Signal Processing, 1994, 7: 1610-1628
[35] Akira T, Mitsuji M, Takao H.“Median and neural networks hybrid filters,”IEEE International Conference on Neural Networks, 1995, 1: 580-583
[36] L. Yin, J. Astola, and Y. Neuvo,“A New Class of Nonlinear Filters-Neural Filters,”IEEE Trans. On Signal Processing, 1983, 3: 1201-1222
[37] P. Maragos, R.W. Schafer,“Morphological filters-part I: their set-theoretic analysis and relations to linear shift-invariant,”IEEE Trans. on Acoustics, Speech, and Signal Processing, 1987, 35(8): 1153-1169
[38] A.Kher and S.Mitra,“Optimum morphological filtering to remove speckle noise from SAR images, image algebra and morphological image processing IV,”Proc. SPIE, 1993, 2030: 97-108
[39] F. Safa and G.. Flouzat,“Speckle removal on radar imagery based on mathermatical morphology,”Signal Processing, 1989, 16(4): 319-333
[40] S. Tsekeridou, C. Kotropoulos, I. Pitas,“Morphological signal adaptive median filter for noise removal,”Proceedings of the Third IEEE International Conference on Electronics, Circuits, and Systems, 1996, 1: 191-194
[41] R. Bernstein,“Adaptive nonlinear filter for simultaneous removal of different kinds of noise in images,”IEEE Translations on Circuits and Systems, 1987, 11: 1275-1291
[42] M. H. Sedaaghi, R. Daj and M.Khosravi.“Mediated morphological filters,”International Conference on Image Processing, 2001: 692-695
[43] J. Oh and L. F. Chaparro,“Adaptive fuzzy morphological filtering of images,”Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1998, 5: 2901-2904
[44] J. Song and E. Delp,“The analysis of morphological filters with multiple structuring elements,”Computer Vision Graphics, Image Processing, 1990, 50: 308-328
[45] Mukhopadhysy S, Chanda B,“An edge preserving noise smoothing technique using multi-scale morphology,”Signal Processing, 2002, 82: 527-544
[46] E. Abreu, M. Lightstone, SK Mitra, and K. Arakawa,“A new efficient approach for the removal of impulse noise from highly corrupted images,”IEEE Translations on Image Process, 1996, 5(6): 1012-1025
[47] Han W, Lin W.“Minimum-maximum exclusive mean(MMEM) filter to removeimpulse noise from highly corrupted images,”Electronics Letters, 1997, 33(2): 124-125
[48] Minggang Ma, Xiao Jiao and Xinquan Tan.“Fuzzy hybrid filter for removal of impulse noise from highly corrupted images,”2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, 2002, 2: 885-888
[49] Mituhiro Okano, Akira Taguchi and Mototaka Sone.“Restoration method of images corrupted by high probability impulse noise by using fuzzy technique,”WCCC-ICSP 2000 5th International Conference on Signal Processing, 2000, 1: 244-247
[50] Shih-Mao Lu, Her-Chang Pu and Chin-Teng Lin.“A HVS-directed neural-network-based approach for impulse-noise removal from highly corrupted images,”IEEE International Conference on Systems, Man and Cybernetics, 2003, 1: 72-77
[51]董继扬,张军英.严重椒盐噪声污染图像的非线性滤波算法.光电子激光[J]. 2003,14(12):1336-1339
[52] XU X. Y., MILLER E. L.“Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images,”IEEE Translations on Image Processing, 2004, 13: 238 -247
[53] Sanjit K. Mitra, Tian-hu Yu.“A new nonlinear algorithm for the removal of impulse noise from highly corrupted images,”IEEE International Symposium on Circuits Systems, 1994, 3: 17-20
[54] Eduardo Abreu, Michael Lightstone, et al.“A new efficient approach for the removal of impulse noise from highly corrupted images,”IEEE Translations on Image Processing, 1996, 5: 1012-1024
[55] M. Ben-Ezra and S. K. Nayar,“Motion-based motion deblurring,”IEEE Translations on Pattern Analysis and Machine Intelligence, 2004, 26(6): 689-698
[56]阮秋琦.数字图像处理学[M].北京:电子工业出版社,2001
[57]高异,梁秀梅,于平义.车辆运动模糊图像的快速恢复[J].计算机工程与应用,2004,2:215-218
[58] YITZHAKY Y., KOPEIKA N. S.,“Identification of blur parameters from motion blurred images,”Graphical Models and Image Processing, 1997, 59(5): 310-320
[59] Yitzhaky Y, Boshusha G, Lem Y, et al.“Vibrated image restoration from a single frame.”SPIE, 1999, 3808: 603-613
[60] Yitzhaky Y, Mor I, Lantzman A, et al.“Direct method for restoration of motion-blurred images,”Journal of Optical Society of America, 1998, 15(6): 1512-1519
[61] Yitzhaky Y, Milberg R, Yohaev S, et al.“Comparison of direct blind deconvolution methods for motion-blurred image,”Applied Optics, 1999, 38(20): 4325-4332
[62] Kopeika-NS S-A.“General restoration filter for vibrated-image restoration,”Applied Optics, 1998, 37(32): 7596-7603
[63]陈前荣,陆启生,成礼智.基于方向微分和加权平均的运动模糊方向鉴别[J].计算机工程与应用,2004,29:1-5
[64]陈前荣,陆启生,成礼智.利用拉氏算子鉴别运动模糊方向[J].计算机应用, 2004,24(9):4-6
[65]王晓红,赵荣椿.匀速直线运动模糊的PSF之估计[J].计算机应用,2001, 21(9):40-41
[66] Cannon M.,“Blind deconvolution of spatially invariant image blurs with phase,”IEEE Transactions Acoustics Speech Signal Processing, 1976, 24(1): 58-63
[67]高梅,陈树越.任意方向运动模糊图像的恢复[J].电脑开发与应用, 2004, 17(7):12-13
[68] M. E. Moghaddam, M. Jamzad.“Finding point spread function of motion blur using radon transform and modeling the motion length,”Signal Processing and Information Technology, 2005: 862-866
[69] A.K.Katsaggelos, Ed., Digital Image Restoration, Springer-Verlag, NewYork, 1991
[70] R. L. Lagendijk, A. M. Tekalp, and J. Biemond,“Maximum likelihood image and blur identificaion: A unifying approach,”Optical Engineering, 1990, 29: 422-435
[71] G.Pavlovic and A. M. Tekalp,“Maximum likelihood parametric blur identification based on a continuous spatial domain model,”IEEE Translations on Image Processing, 1992, 1: 496-504
[72]鲜飞.贴片机视觉对中系统[J].电子工艺技术,2005,26(5):150-154
[73] Michael K.Bartschet, Joseph S.Kovalchick.“Method and apparatus for chip placement,”United States Patent: 4980971, 1991
[74]宋福民,张小丽,马如震.SMT2505全视觉多功能贴片机的研制[J].电子工业专用设备,2002,31(4):219-223
[75] A.Yamauchi, Y.Arai.“Analysis and measures against heat-expansion for sub-micron LD assembly by passive alignment,”51st Electronic Components and Technology Conference. USA, Orlando: IEEE, 2001: 242-246
[76] Uwe Tews, Maximilian R.Rottmann.“Method and an apparatus for the positioning ofcomponents with reference to a workpiece,”United States Patent: 4615093, 1986
[77] Edison T. Hudson.“Method and apparatus for reflective in-flight component registration,”United States Patent: 5768759, 1998
[78] Edison T. Hudson.“One camera system for component to substrate registration,”United States Patent: 20010055069, 2001
[79] Daniel Link.“Appratus for mounting a flipchip on a work piece,”United Stated Patent: 6530146, 2003
[80] Tukey J. Exploratory Data Analysis. Addison-wesley, Mass 1971: 236-281
[81] J. Serra. Image analysis and mathematical morphology. Academic Press, London, 1982
[82] J. Serra,“Morphological filtering: an over view,”Signal Processing, 1994, 38(1): 3-11
[83] Sternberg S. R., Grayscale morphology. Computer vision, Graphics, and Image Processing. 1986
[84] Image Processing Toolbox, Matlab Help
[85] Xinhua Zhuang,“Decomposition of morphological structuring elements,”Journal of Mathematical Imaging and Vision, 1994, 4(1): 5-18
[86] Jones R., Svalbe I.,“Basis decomposition of morphological operations,”Pattern Recognition, 1992, 3: 264-267
[87]孙兆林.MATLAB 6.x图像处理[M].北京:清华大学出版社,2002
[88]张秉仁,陈里铭,高游.基于图像运动的图像模糊机理与恢复技术[J].计算机工程. 2004,30(8):21-22
[89]李伟光,曾志军,曾志新,邹海明.表面组装装备机器视觉系统研究[J].组合机床与自动化加工技术,2005,9:66-67,70
[90]杨晓飞,韩昌元.利用离轴三反镜光学系统确定各镜的装调公差[J].光学技术,2005,31(2):173-176
[91] A. K. Katsaggelos, J.Biemond, R.M.Mersereau, R. WSchafer.“A general formulation of constrained iterative restoration algorithms,”IEEE International Conference on Acoustics, Speech, and Signal Processing, 1985, 10: 700-703
[92] Steven S. O. CHOY, Yuk-Hee CHAN, Wan-Chi SIM.“New adaptive iterative image restoration algorithm,”IEEE International Conference on Image Processing, 1994, 2: 670-674
[93] J. W. Woods and V. K. Ingle.“Kalman filtering in two-dimensions: further results,”IEEE Trans. Acoust. , Speech, Signal Processing. 1981, 29(2): 188-197
[94] N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MITPress, Cambridge, MA, 1942
[95] Euresys,几何特征模板匹配.http://www.euresys.com/CN/Products/EasyFindCn.asp
[96]邸慧,丁晓华,于起峰.基于Z变换匀速旋转运动模糊图像的快速恢复[J].光电工程,2006,33(4):89-92,110
[97]洪汉玉,张天序.非零边界旋转运动模糊图像的恢复算法[J].中国图象图形学报,2004,9(3):265-274
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