CCD细分技术及其应用研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
电荷耦合器件CCD(Charge Coupled Devices)是20世纪70年代初发展起来的新型半导体集成光电器件,它是美国贝尔电话实验室的W. S. Boyle和G. E. Smith于1970年首先提出的。近30年来,依靠已经成熟的MOS集成电路工艺,CCD器件及其应用技术得以迅速发展。CCD器件按其感光单元的排列方式分为线阵CCD和面阵CCD两类,但无论是线阵CCD还是面阵CCD,由于其固有的物理特性、工作机理等原因以及芯片结构、制作工艺等的限制,CCD像素精度不能做得很高。为了保证CCD像元具有足够的感光面积和防止相邻像元之间的互相串扰,所有CCD器件的像元面积以及像元间距都不能做得太小,目前一般在微米级别,这就限制了CCD在高精度测量领域中的应用。
     CCD图像测最系统主要由照明系统、被测物体、光学成像系统、信号处理电路和计算机组成,因此影响CCD测量精度的因素主要有:CCD像元的制造误差,CCD对光照度的分辨率,A/D转换的精度,照明系统的不稳定性,CCD驱动电路的附加噪声,成像系统调焦误差,成像系统的误差(如球差、像差等),外界环境的影响(如温度、振动等)以及图像处理水平和软件算法优劣等。为了提升CCD测量水平,可以选用高分辨率的CCD器件和采样频率比较高的图像采集卡,或进行光学系统的放大,或者采用特殊的光源进行照明等。但这些方法的使用一般会受到某种程度的限制和影响,如CCD像素制造精度不能做得很高;采用光学系统放大时像的质量会下降;光源的稳定性均匀性对CCD成像质量的影响,而通过CCD图像处理和软件算法来提高CCD测量精度是一种简单方便而又行之有效的方法。为了提高CCD应用系统的测量精度,满足CCD在高精度测量领域的应用,人们提出CCD细分,同时开始与此相关的CCD细分技术与应用的研究。
     CCD细分技术在图像处理领域中应用较多,但在高精度的测量测控领域中还需要进一步的研究和发展,CCD细分技术在高精度计量测试领域中的研究和应用是本论文的主要研究方向。本文从CCD基本结构和工作原理的介绍开始。对影响CCD测量精度的各个方面进行了分析,对CCD测量中的照射光源和光学处理系统,CCD视频信号处理与数据采集、CCD工作信号的噪声分析与处理、CCD边缘检测与像点定位等几个方面进行了详细的研究与探讨。同时,还建立了一套高精度微动平台和定位检测装置,通过实验手段来研究和比较各种CCD细分方法和技术,对不同CCD细分算法以及亚像素定位算法等进行了较为详尽的理论研究和实验检测,提出了一系列新的细分方法与技术,并将CCD细分技术应用到高精度CCD测量产品的开发中去。
     在本论文研究中,将CCD细分基本分为边缘检测细分和像点定位细分两种研究思路,对这两种思
Charge Coupled Devices(CCD) is the new type semiconductor integrated photoelectric device developed at the beginning of the seventies of the 20th century. It was put forward at the first of 1970 by W.S.Boyle and GE.Smith from the American Bell telephone laboratory. In the past thirties years, CCD device and its application technology have been developed rapidly with the developed MOS integrated circuit craft. CCD. device falls into linear CCD and matrix CCD according to its sensitization unit permutation way. No matter linear CCD or matrix CCD, the precision of CCD image cell cannot be made very high for the reasons such as its inherent physical characteristic and working mechanism ,etc, or the restrictions as the structure of the chip and manufacture technology ,etc. In order to ensure CCD image cell having adequate sensitization matrix and prevent the commutative disturbing of close image cell, the image cell area and the space of all the CCD devices cannot be so small, in one micron of ranks generally at present. So CCD is restricted to the application of high accuracy measure field.CCD image measure system is composed of lighting system, testee, optic imaging system, signal processing circuit and computer. So, the factors influencing CCD measure accuracy is: the manufacture error of CCD image cell, CCD resolution ratio to the illumination, the accuracy of A/D transforming, the unstability of lighting system, additional noise of CCD driver circuit, focusing error of the imaging system(for instance the sphere error, difference, etc.), the influence of external environment(such as temperature, oscillate, etc.), the image processing level and the goodness or badness of software algorithms, etc. In order to improving the CCD measure level, the high resolution CCD device and high sampling frequency image collecting board is adopted, or the optic enlarging system carried on and the lighting with special lighting source used. But the adoption of these method can be commonly restricted and influenced to some certain degree, such as the precision of CCD image cell not so higher, the imaging quality declined after adopting optic enlarging system, CCD imaging quality affected by the stability and equality of
    lighting source. But it is simple and effective to improve CCD measure precision by using CCD image processing and soft algorithms. The CCD subdivision subject is put forward and the relative research of CCD subdivision technology and application started at the same time in order to improve the measure precision of CCD application system and meet the demand of CCD application on high precision measure field.With the beginning of introduction on CCD basic structure and working principle in the paper, the various aspects that influence CCD measure precision is analyzed, lighting source and optic processing system, CCD video signal processing and data collection, the noise analysis and processing of CCD working signal, CCD edge detection and image spots location, etc. researched and discussed in detail. Meanwhile, a set of high accuracy micrometer platform and position detection installation is build up. Various CCD subdivision method and technology can be researched and compared by the means of experiment. The academic research and experimental detection of the different CCD subdivision algorithms and subpixel position algorithms is carried on in detail, a series of new subdivision method and technology provided, and the CCD subdivision technology put into the application of high precision measure product development.CCD subdivision can be basically divided into two kinds of research scheme on edge detection subdivision and image spots location subdivision in the paper. Edge is the most principle characteristic of CCD image, the analysis and discussion about two schemes is the main research content of CCD subdivision technology. In the research of CCD edge detection subdivision, the method of dynamic probability > modulation and demodulation> spatial fitting functionn polynominal interpolation, etc. are analyzed theoretically, and the mechanism of these method is inferred on the mathematical aspect, and the advantage and insufficiency summarized. The fuzzy imaging method is introduced fundamentally and checked up experimentally.CCD image spots position subdivision method is introduced between matrix CCD and linear CCD. The matrix CCD subdivision position algorithm is the main method of centroid> power-added centroidx Gauss curve area fittings parabola curve area fitting. The linear CCD subdivision location algorithm have the one-dimension linearity compensation interpolation method, centroid interpolation method, ending voltage method, proportional centrality method, centre of gravity method, step-method, etc. These algorithms is to utilize the high relativity of gray degree value between the image elements for object image spots, and construct mathematics model that reflects accurately the object area image elements gray degree value,
引文
[1] 王庆有,孙学珠.CCD应用技术[M].天津:天津大学出版社,2000,11[1]
    [2] 金杰,徐锡林.提高CCD分辨率的一种尝试.现代计量测试,1997,3
    [3] Emest O. Doebelin, Measurement System Application and Design, Fourth Edition, USA: Mc Graw-Hill Inc, 1990, 09
    [4] 郑友琴,袁旭军.用CCD实现精密的长度测量.电测与仪表,1998,2
    [5] 湛延政,吕海宝.CCD细分技术方法研究及应用.光学学报,2002,11
    [6] 魏仲慧.数字光电自准直仪的研究与设计.光学精密工程,1996,6
    [7] Fast and flexible CCD-driver system using fast DAC and FPGA Miyata, Emi; Elsevier Science S0168-9002(00)01015-9
    [8] Investigation of the radiation performance of CCD sensors in a vertex detector application Stefanoy, K. D Elsevier Science S0168-9002(99)00618-X
    [9] D Mendlovic, A W Lohmann. One-dimensional super-resolution Optional system for temporally restricted objects[J]. Applied optics, 1997.36(11): 2353-2359
    [10] L Wald, T Ranchin. Fusion of images and rester-maps of different spatial resolutions by enerustation: an improved approach[J]. Computers, Environment and Urban System, 1995, 19(2): 77-87
    [11] H B Manjunatt. S K Mitra. Multisensor image fusion using the wavelet transform[J]. Graphical Models and Image Processing, 1995, 57(3): 235-245
    [12] 郑友琴,袁旭军等.用CCD实现精密的长度测量.电测与仪表,1998,2
    [13] 蒋剑良,孙雨南.线阵CCD位移测试技术的误差分析.计量技术,2002,7
    [14] 庞长富.CCD摄像机用于测量中存在的问题及解决方法.光学技术,1996,(2):5~8
    [15] 雷玉堂,王庆有.光电检测技术.中国计量出版社,1997
    [16] 雷玉堂.电视监控系统(上).中国计量出版社,2002
    [17] 袁文博编译.闭路电视系统设计与应用.电子工业出版社,1986
    [18] [美]赫曼.克鲁格著,赵希锋译.CCTV完全手册.香港泰兴科技书局,1999
    [19] 杨磊、李峰.闭路电视监控系统.机械工业出版社,1999
    [20] 周太明.光源原理与设计.复旦大学出版社
    [21] 金国藩,李景镇.激光测量学.北京:科学出版社,1998.8
    [22] 杨淋,周肇飞等.线阵CCD在高精度测量系统中的照明选择.实用测试技术,2002,9
    [23] 郭华,邵东向等.CCD输出信号的电处理方法.传感器技术,1998,1
    [24] 王新成.多媒体实用技术.成都:电子科技大学出版社,1996
    [25] 贺忠海,王宝光等.利用曲线拟合方法的亚像素提取算法.仪器仪表学报,2003,4
    [26] 吴晓波等.应用多项式插值函数提高面阵CCD尺寸测量的分辨力.仪器仪表学报,1996(2):154~158
    [27] 姜凌涛.应用Marr算子实现线阵CCD边缘高分辨率定位.光电工程,1996(1):23~28
    [28] 王和顺,陈华.一种提高CCD测量精度的新方法[J].四川大学学报,2001,33(5):63~65
    [29] Peter S. Optical super-resolution using solid-state cameras and digital signal processing[J]. Opt Eng, 1988, 27(7): 534-540
    [30] Kisworo M, Venkatesh S. Modeling edges at subpixel accuracy using the local energy approach[J].IEEE Trans. On PAMI, 1994, 16(4): 405-410
    [31] 贺忠海,王宝光等.理想边缘产生方法的研究.光学精密工程,2002,2
    [32] 杨博雄,傅辉清等.一种线阵CCD时序仿真的新方法.大地测量与地球动力学.2004,Vol.24,No.3:124-127.
    [33] 杨博雄,张晓华等.线阵CCD的高速数据采集与存储.大地测量与地球动力学.2004,Vol.24,No.2:124-127.
    [34] 曾雄文,陆启生等.CCD的光电特性研究.强激光与粒子束,1999,2
    [35] 姜忠宝,高俊国等.CCD图像传感器的原理及军事应用.电子元器件应用,2003,1
    [36] 张展霞,刘洪涛等.电荷耦合器件及其应用进展.光谱学与光谱分析,2004(4)
    [37] 程开富,CCD图像传感器的市场与发展.国外电子元器件,2009(7)
    [38] 雷玉堂、罗辉.面阵CCD摄像机的误差及检校.1995年全国第六届光电技术及系统学术会论文集
    [39] 潘东超、雷玉堂、卢健.单CCD相机人体运动三维测量系统的研究.武汉测绘科技大学硕士研究生学位论文(92年)
    [40] 徐春斌、卢健、冯文灏.基于结构光单CCD相机的物体表面三维自动测量系统的研究.武汉测绘科技大学硕士研究生学位论文(94年)
    [41] Wen-DD. Advanced charge-coupled device(CCD) line imaging devices Proceeding of the Society of Photo Optical Insturmentation-Engineers. Vol. 203; 1979;
    [42] Kobayashi-K; Sawada-G. High-speed digital facsimile equipment dcvelopment. Revies of the Electrical Communication Laboratories. Vol. 28, 1980(1-2)
    [43] 成光.像敏CCD的研制进展及市场前景.电子与自动化,1998(3)
    [44] 蔡文贵,李永远等.CCD技术及应用.电子工业出版社,1992.11
    [45] 袁祥辉主编.固体图像传感器.重庆大学出版社,1996.3
    [46] 周祖成,茅於海.电荷耦合器件在信号处理图像传感器中的应用.清华大学出版社,1987.12
    [47] 杨博雄,傅辉清等.CCD工作信号的噪声与处理.光学与光电技术.2004,Vol.2,No.4.:51-53.
    [48] Ronald G. Driggers et al. Introduction to Infrared and Electro-Optical Systems[M]. London: Artech Hourse, Inc, 1999.
    [49] 郭彦珍.应用概率论尺寸测量分辨力的解调法.仪器仪表学报,1986(1):149~153
    [50] 邹仲力.提高CCD尺寸测量分辨力的解调法,仪器仪表学报,1986(1):38~44
    [51] 张晓等.利用空间拟合函数实现超像元边界检测.计量学报,1996(3):236~240
    [52] 高雁飞.几种CCD图像边缘的高精度检测方法分析.西安工业学院学报,1998,6
    [53] 俞巧云,刑晓正等.直线拟合方法在一维图像边缘检测中的应用.光电工程,2001,12
    [54] Stanto, R. H., et al. ASTROS: A sub-arcsec C C D star track[J]. SPIE, 501(State of the Art Imageing Arrays and Their Application)(1984)
    [55] Ju, Gwanghyeok. Autonomous star sensing, pattern identificaion, and attitude determination for spacecraft: An analytical and experimental stud[D]. Texas A & M Uniniversity, 2002.
    [56] Liebe, C. C. Accuracy Performance of Star Track-ers-a Tutorial Aerospace and Electronic Systems[J]. IEEEE Transactions on, 2002, 38(2): 587-599
    [57] 杨博雄,陈英.数字图像的预处理方法研究.咸宁职业技术学院学报.2004,Vol.1.No.1.:6-8.
    [58] 闻路红,童卫旗等.重心算法确定CCD像点位置的硬件实现.仪表技术与传感器.2004,5.p42-43
    [59] 张志利,李国英.阶梯法在线阵CCD位置检测中的应.传感器技术.2004.6,Vol.23
    [60] 蒋月娟,夏亮.一种精密测量CCD上像点位置的方法.光学仪器.1999.6,Vol.21,No.6
    [61] 张晓等.利用空间拟合函数实现超像元边界检测.计量学报.1996(3)
    [62] 戴政远等.时空细分新技术与计算机实现原理的研究.电子与自动化.1997.4
    [63] 冷何英,戴俊钊.高精度图像测量系统中的细分方法.光电工程增刊.1999
    [64] JANESICK J R, TOM E, STEWART C, et al. Scientific charge-coupled devices[J]. Optical Engineering, 1987, 26 (8): 692-714
    [65] SCHULZ M, CALDWELL L. Nonuniformity correction and correctability of infrared focal plane arrays[J]. Infrared Physics & Technology, 1995, 36: 763-777.
    [66] 李仰军,马俊婷.微光CCD相机的噪声分析与处理[J].应用基础与工程科学学报,2001,9(2-3):277-282
    [67] Martin G J, Blessinger K V, Veits C. High-resolution CCD camera for industrial imaging. SPIE, 1901: 2-11
    [68] Doty J P, Lupptino G A, Richer G R. Design of low noise, high performance X-ray charge-coupled device camera. Optical Engineering, 1987, 26: 829-836
    [69] MeCurnin T W, Schnoley L C, Sims G R. Signal processing for low-light-level, high-precision CCD imaging.
    [70] 陈邓云,沈忙作.阵列探测器的象点亚象素定位精度.光学学报,1993,13(10):952-953
    [71] 杨华中,汪惠.数值计算方法与C语言函数库.北京:科学出版社,1996
    [72] 沙定国.实用误差理论与数据处理.北京:北京理工大学出版社,1993
    [73] Lyvers E P, Mitchell O R. Subpixel measurement using a moment-based edge operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, No. 11: 1293-1309
    [74] Hachicha A, Simon S. Subpixel edge detection for precise measurements by a vision system. SPIE, 1988, Vol. 1010: 148-157
    [75] Dvornichenko V N. Bounds on correlation function with application to registration[J]. IEEE Trans. Pattern Anal. Machine Intell. 1983, 5(2): 206-213
    [76] Goshtasby A, Gage S H, Bartholic I F. A two-stage cross correlation approach to template matching[J]. IEEE Trans Pattern Anal. Machine Intell, 1984, 6(3): 374-378
    [77] Brown L G. A Survey of image registration techniques[J]. ACM Computing Surveys, 1992, 24(4): 325-376
    [78] 杨博雄,肖茵.超级CCD的基本原理与关键技术.传感器世界.2005,Vol.11,No.2:25-28.
    [79] Parker J A, Kenyon R V, Donald E T. Comparison of interpolating methods for image resampling[J]. IEEE Trans on Medical Imaging, 1983, MI-2(1): 31-39
    [80] Hughlett R E, Coop K A. A video based alignment system for X-ray lithography[C]. In: Electro-bearn, X-ray and Ion-Bean Submicrometer Lithographies for Manufacturing, SPIE, 1991, 1465: 100-110

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700