基于提升小波变换的自动聚焦算法研究
|
摘要
随着时代的进步,我们对各类图像设备(如数码相机、摄像机、影像监视系统和显微镜等)的需求日益增加,自动聚焦技术的研究也变得越来越重要。自动聚焦技术可以使成像设备自动调节直至聚焦以获取清晰的图像,是数字成像过程中的关键技术。自动聚焦技术的发展程度直接影响着图像设备的成像速度和成像质量,因此这项技术的研究具有十分重要的意义。
在当前的自动聚焦研究中,聚焦算法主要分为离焦深度法和聚焦深度法两大类。离焦深度法是在离焦图像中直接获取目标物体深度信息的方法,其经过计算得到镜头与目标物体的距离,然后调整镜头到精确聚焦的位置,实现聚焦。聚焦深度法是基于搜索基础的聚焦策略,其通过对多幅图片进行清晰度判断来驱动镜头移动,直至镜头到达精确聚焦点,是当今研究的主流方向,本文也是以聚焦深度法基础的。
在本文中,首先对自动聚焦系统的工作原理进行了简单的介绍,数字式自动聚焦系统首先利用感光元件对目标物体进行成像,然后在图像中选择感兴趣区域作为聚焦区域,并用清晰度评价函数对该区域的图像清晰度进行评价,进而根据最大极值搜索法来驱动镜头移动至准确聚焦的位置。自动聚焦系统主要包括三个关键问题：聚焦区域(ROI)的选择,图像清晰度评价函数的选择,和全局最大值搜索算法的选择。在论文接下来的内容中,对这三个问题分别进行了分析。
根据对现存的自动聚焦系统关键问题的分析,从聚焦实时性和精确性的角度出发,本论文提出了一种基于提升小波变换的聚焦策略。小波变换是基于有限宽度基函数进行变换的,具有分析频率可调的优点。与傅里叶变换、余弦变换相比,小波变换具有检测图像尖锐边缘处灰度值突变的能力,所以基于小波变换的图像清晰度评价函数可以更好地分辨出图像质量的好坏。提升小波又称第二代小波,其把现存的有限长的小波滤波器分解,加快了小波变换的速度,不再需要二进小波函数平移和伸缩的条件,不依赖于傅里叶变换,但仍具有第一代小波的所有优点,本文中采用提升小波变换进行评价函数的计算。
在提出的聚焦策略中,首先利用人工鱼算法选择聚焦窗口,然后在该区域内利用梯度函数作为评价函数,先以自适应大步长穷举搜索算法搜索聚焦点所在区间,然后改用改进后的捉升小波评价函数以小步长爬山搜索算法搜索聚焦点准确位置。在研究过程中采用该聚焦策略对大量的图像序列进行了实验,实验表明,无论目标物体是否在视野中央都能实现精确聚焦,且与传统的小波算法相比聚焦时间可以缩减30%-40%。经初步实验验证,该算法可以避免聚焦点搜索中陷入局部最优的缺陷,具有一定的自适应能力,实时性和准确性也得到了优化。
Along with the progress of times, our need to each kind of digital equipments (such as digital camera, DV and image surveillance system and microscope etc.) increases day by day, and the research of auto-focusing also becomes more and more important. Auto-focusing is a key technique in the process of accessing digital pictures, it can make the digital equipments obtain a clear digital picture of the target object automatically. The degree of auto-focusing development directly influence the speed and quality of the digital equipments to get pictures, so the technique of auto-focusing is attached with significance.
The current research of auto-focusing is always divide into two kinds:depth from defocus (DFD) and depth from focus (DFF). DFD obtains the focal depth of object target directly from defocus images, it moves the lens to the focused position with the calculation of the distance between lens and object target. DFF is established on the searching pattern, it moves the lens through the information get from the quality of several pictures. DFF is the essential direction of the research, and this text is also based on it.
In this text, the principle of auto-focusing system is simply introduced first. Digital auto-focusing system first catches a picture of target object, then select only one small part of the picture as the auto-focusing region and use a focus measure function (FMF) to evaluate the image quality which determine how to move the lens to the focusing position. The auto-focusing system mainly includes three parts:the region of interest (ROI) selection, the focus measure function selection and the maximum searching algorithm selection. The three questions are respectively analyses in the contents behind.
According to the analysis of key matters in auto-focus system, the text put forward a new strategy from the point of real-time and precision. Wavelet Transform is based on limited width basis function, it can analyze a signal through adjustable frequency that it can examine the gray changes at the picture edges. Compared with Fourier Transform and Cosine Transform, the Wavelet Transform can examine the picture quality more precisely. Lifting Wavelet Transform (the second generation DWT) divide the existing limited long filter and no longer rely on the Fourier Transform and the condition of translational and flexible. Compared with the first generation DWT. Lifting DWT inherit all the advantages of DWT and it can transform faster than the first generation DWT. According to the characteristics, the text compute the evaluation function through Lifting DWT.
The improved auto-focus strategy put forward in the text first selects the ROI with PSO searching algorithm, then uses gradient evaluation function to evaluate the picture quality and searches the focused position with average searching algorithm by big steps. When find the small zone which the focused position lies on, change to use DWT evaluation function to evaluate the picture quality and search the focused position with mountain-climbing searching algorithm by small steps. In the course of the study, many experiments were carried out on the image sequence using the focusing strategy. The results show that, regardless of whether the object is in the center of the field, this strategy can achieve focusing precisely and the focusing time can be reduced30%-40%compared with the traditional wavelet algorithm. According to the experience results, the new auto-focus strategy can avoid the lens find the local focused positon, and it can decrease the time by searching the ROI automatically. The digital picture equipments can achieve auto-focused fastly and accurately with the new strategy.
在当前的自动聚焦研究中,聚焦算法主要分为离焦深度法和聚焦深度法两大类。离焦深度法是在离焦图像中直接获取目标物体深度信息的方法,其经过计算得到镜头与目标物体的距离,然后调整镜头到精确聚焦的位置,实现聚焦。聚焦深度法是基于搜索基础的聚焦策略,其通过对多幅图片进行清晰度判断来驱动镜头移动,直至镜头到达精确聚焦点,是当今研究的主流方向,本文也是以聚焦深度法基础的。
在本文中,首先对自动聚焦系统的工作原理进行了简单的介绍,数字式自动聚焦系统首先利用感光元件对目标物体进行成像,然后在图像中选择感兴趣区域作为聚焦区域,并用清晰度评价函数对该区域的图像清晰度进行评价,进而根据最大极值搜索法来驱动镜头移动至准确聚焦的位置。自动聚焦系统主要包括三个关键问题：聚焦区域(ROI)的选择,图像清晰度评价函数的选择,和全局最大值搜索算法的选择。在论文接下来的内容中,对这三个问题分别进行了分析。
根据对现存的自动聚焦系统关键问题的分析,从聚焦实时性和精确性的角度出发,本论文提出了一种基于提升小波变换的聚焦策略。小波变换是基于有限宽度基函数进行变换的,具有分析频率可调的优点。与傅里叶变换、余弦变换相比,小波变换具有检测图像尖锐边缘处灰度值突变的能力,所以基于小波变换的图像清晰度评价函数可以更好地分辨出图像质量的好坏。提升小波又称第二代小波,其把现存的有限长的小波滤波器分解,加快了小波变换的速度,不再需要二进小波函数平移和伸缩的条件,不依赖于傅里叶变换,但仍具有第一代小波的所有优点,本文中采用提升小波变换进行评价函数的计算。
在提出的聚焦策略中,首先利用人工鱼算法选择聚焦窗口,然后在该区域内利用梯度函数作为评价函数,先以自适应大步长穷举搜索算法搜索聚焦点所在区间,然后改用改进后的捉升小波评价函数以小步长爬山搜索算法搜索聚焦点准确位置。在研究过程中采用该聚焦策略对大量的图像序列进行了实验,实验表明,无论目标物体是否在视野中央都能实现精确聚焦,且与传统的小波算法相比聚焦时间可以缩减30%-40%。经初步实验验证,该算法可以避免聚焦点搜索中陷入局部最优的缺陷,具有一定的自适应能力,实时性和准确性也得到了优化。
Along with the progress of times, our need to each kind of digital equipments (such as digital camera, DV and image surveillance system and microscope etc.) increases day by day, and the research of auto-focusing also becomes more and more important. Auto-focusing is a key technique in the process of accessing digital pictures, it can make the digital equipments obtain a clear digital picture of the target object automatically. The degree of auto-focusing development directly influence the speed and quality of the digital equipments to get pictures, so the technique of auto-focusing is attached with significance.
The current research of auto-focusing is always divide into two kinds:depth from defocus (DFD) and depth from focus (DFF). DFD obtains the focal depth of object target directly from defocus images, it moves the lens to the focused position with the calculation of the distance between lens and object target. DFF is established on the searching pattern, it moves the lens through the information get from the quality of several pictures. DFF is the essential direction of the research, and this text is also based on it.
In this text, the principle of auto-focusing system is simply introduced first. Digital auto-focusing system first catches a picture of target object, then select only one small part of the picture as the auto-focusing region and use a focus measure function (FMF) to evaluate the image quality which determine how to move the lens to the focusing position. The auto-focusing system mainly includes three parts:the region of interest (ROI) selection, the focus measure function selection and the maximum searching algorithm selection. The three questions are respectively analyses in the contents behind.
According to the analysis of key matters in auto-focus system, the text put forward a new strategy from the point of real-time and precision. Wavelet Transform is based on limited width basis function, it can analyze a signal through adjustable frequency that it can examine the gray changes at the picture edges. Compared with Fourier Transform and Cosine Transform, the Wavelet Transform can examine the picture quality more precisely. Lifting Wavelet Transform (the second generation DWT) divide the existing limited long filter and no longer rely on the Fourier Transform and the condition of translational and flexible. Compared with the first generation DWT. Lifting DWT inherit all the advantages of DWT and it can transform faster than the first generation DWT. According to the characteristics, the text compute the evaluation function through Lifting DWT.
The improved auto-focus strategy put forward in the text first selects the ROI with PSO searching algorithm, then uses gradient evaluation function to evaluate the picture quality and searches the focused position with average searching algorithm by big steps. When find the small zone which the focused position lies on, change to use DWT evaluation function to evaluate the picture quality and search the focused position with mountain-climbing searching algorithm by small steps. In the course of the study, many experiments were carried out on the image sequence using the focusing strategy. The results show that, regardless of whether the object is in the center of the field, this strategy can achieve focusing precisely and the focusing time can be reduced30%-40%compared with the traditional wavelet algorithm. According to the experience results, the new auto-focus strategy can avoid the lens find the local focused positon, and it can decrease the time by searching the ROI automatically. The digital picture equipments can achieve auto-focused fastly and accurately with the new strategy.
引文
[1]白廷柱，金伟其.光电成像原理与技术[M].北京：北京理工出版社,2006：P5-7.
[2]卜坤亮，王建东,樊玮虹等.基于FPGA芯片的超声波红外线测距系统[J].微处理机,2006,Vol.2：P88-90.
[3]Subbarao, Nikzad. Focusing techniques [J]. Opt. Eng,1993.
[4]黄剑琪.基于频谱分析的数字对焦技术研究[D].杭州：浙江大学硕士学位论文,2001：P4-6.
[5]原育凯.光学系统的自动调焦方法[J].红外与激光工程,2004：P15-21.
[6]Tse-Chtmg Wei, Murali Slabbarao. Continuous focusing of objects using DFDIS [J].2008, Vol.2183.
[7]王立强,林斌,陆祖康等.基于USB Video Camera的自动对焦系统[J].光电工程,2001,Vol.28(5)：P32-34.
[8]I.Raveh, D.Mendlovic, Z.Zatevsky, A.W.Lohraann. Digital method for defocus corrections:experimental results [J]. Optical Engineering,1999, Vol.38(10): P116-119.
[9]E.Krotkov. Focusing [J], International Journal of Computer Vision.1987, Vol.1: P223-237.
[10]Jie He, Rongzhen Zhou, Zhiliang Hong. Modified Fast Climbing Search Auto-focus Algorithm with Adaptive Step Size Searching Technique for Digital Camera[J]. IEEE Transactions on Consumer Electronics,2003, Vol.49(2): P257-262.
[11]徐洲.一种基于图像处理的自动聚焦方法的研究[D].中国科学院西安光学精密机械研究所,2003.
[12]张乐,姜威,高赞.数字图像一阶矩的自动聚焦区域选择算法[J].光学技术,2008,Vol.34(2)：P163-169.
[13]Kong-feng Zhu,Wei Jiang. Using Non-uniform Sampling to Make Automatic Focusing Result Correct [C]. IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications Proceedings,2005:P28-31.
[14]王倩，宋恩民，许向阳等.基于加权邻域相关性的显微镜自动聚焦函数[J].光学精密工程,2008,Vo1.29(1)：P53-55.
[15]蒋海华.基于图像清晰度评价函数的显微镜自动调焦技术研究[J].光学技术.2008，Vo1.34：P284-285.
[16]白磊.CCD显微测量系统自动调焦技术的研究[D].哈尔滨工业大学硕士学位论文,2002.
[17]Mason DC,Green DK. Automated Focusing of a Computer Controlled Microscope [C]. IEEE Trans Biomed Eng BME,2010, Vol.22:P137-150.
[18]Wang G, LChen D W, Tao CD. Research on Auto-focusing system Based on Image Processing[J]. SPIE,2008, Vol.3650:P88-92.
[19]Je-Ho Lee, Kun-Sop Kim, Bung-Deok Nam. Implementation of a Passive Automatic Focusing Algorithm for Digital Still Camera[C]. IEEE Transactions Consumer Electronics,2005, Vol.43(3):P421-429.
[20]Wang G, LChen D W, Tao CD. Research on Auto-focusing system Based on Image Processing[C]. SPIE.2006, Vol.3650:P88-92.
[21]夏德深,傅德盛.现代图像处理技术与应用.南京：东南大学出版社，1997.
[22]Shree K. Nayar. Yasuo Nakagawa. Shape from Focus[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1994, Vol.16(8):P824-831.
[23]朱孔凤.姜威.高赞.自动聚焦系统中聚焦窗口的选择及参量的确定[J].光学学报,2006.Vo1.26(6)：P777-780.
[24]Won C S, Pyan K. Automatic object segmentation in images with low depth of field [C]. Proc IEEE Int Conf on Image Proc.2002:P805-808.
[25]Yao Y, Abidi B, Doggaz N. Evaluation of sharpness measures and search algorithms for the auto-focusing of high magnification images[J]. Visual Information Processing,2006, Vol.62(46):P1-12.
[26]J J Liang, AK Qin, P N Suganthan, S Baskar. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multi-modal Functions [J]. IEEE Transactions on Evolutionary Computation Science (S1089-778X),2006, Vol.10(3): P281-295.
[27]高尚,杨静宇.群智能算法及其应用[M].北京：中国水利水电出版社,2006：P2-17.
[28]Eberhert R, Kennedy J. A New Optimizer Using Particle Swarm Theory [C]. Proc 6 Intl, Piscataway, Symposium on Micro Machine and Human Science, IEEE Service Center,1995:P1942-1948.
[29]Nanbo Jin, Yahya Rahmat. Advances in Particle Swarm Optimization for Antenna Designs:Real-Number, Binary, Single-Objective and Multi-objective Implementations [J]. IEEE Transactions on Antennas and Propagation. Science (S0018-926X),2007, Vol.53(3):P556-567.
[30]Said M Mikki, Ahmed A Kishk. Quantum Particle Swarm Optimization for Electromagnetic[J]. IEEE Transactions on antennas and propagation. Science (S0018-926X),2006. Vol.54(10):P2764-2775.
[31]李奇,徐之海.数字成像系统自动对区域设计[J].光子学报,2003,Vol.32(12)：P1499-1501.
[32]姜志国,韩冬兵,袁天云.基于全自动控制显微镜的自动聚焦算法研究[J].中国图形图像学报,2004,Vo1.9(4)：P397-401.
[33]P.T. Yap. P. Raveendran. Image focus measure based on Chebyshev moments[J]. IEEE Proc.-Vis. Image Signal Process,2004, Vol.15(2):P128-136.
[34]N.Kehtarnavaz, H.J.Oh. Development and real-time implementation of a rule-based autofocus algorithm [J]. Real-Time Imaging,2003. Vol.9:P197-203.
[35]M. Subbarao, T.S. Choi. A. Nikzad. Focusing techniques [J]. Optical Engineering, 1993. Vol.32:P 2824-2836.
[36]YOON K K, JUNE S L. ALDO W M. A video camera system with enhanced zoom tracking and auto white balance [J]. IEEE Transactions on Consumer Electronics, 2002,Vol.48(3):P428-434.
[37]周贤,姜威.基于图边缘能量的自动聚焦算法[J].光学技术,2006，Vol.32(2)：P213-215.
[38]Yi Yao, Besma Abidi, Narjes Doggaz. Evaluation of Sharpness Measures and Search Algorithms for the Auto- focusing of High Magnification Images[J]. Visual Information Processing,2006, Vol.(6246):P1212-1217.
[39]L. Firestone, K. Cook, K. Culp. Comparison of autofocus methods for automated microscopy [J].1991, Vol.12:P195-206.
[40]Lee SY, Kumar Y, Cho JM, Kim SW. Low-power passive focus measure operator based on the DCT for mobile phones[J]. The Imaging Science Journal,2008. Vol.56(6):P301-306.
[41]郑玉珍,吴勇.倪旭翔.实时自动对焦的研究[J].光电工程.2004,Vol.31(4)：P64-66.
[42]于保军,杨志刚,田丰君.宏/微双驱动定位台显微视觉自动聚焦清晰度评价方法[J].农业机械学报,2005，Vo1.6(4)：P552-555.
[43]李明明，王新赛.李坚.基于图像处理的自动聚焦清晰度函数比较研究.第九届全国光电技术学术交流会,2007,Vol.12(5)：P537-542.
[44]郭丙华,廖启亮,余志.基于小波变换的快速自动聚焦算法[J].中山大学学报(自然科学版).2007,Vo1.46(2)：P12-15.
[45]陈晓彬.基于小波的自动聚焦算法研究[J].通信技术,2009,Vo1.43(6)：P210-212.
[46]Yang G, Nelson BJ. Wavelet-Based autofocus- using and unsupervised segmentation of microscope images[C]. Proc. of the 2003 IEEE/RSJ Intl.Conference on Intelligence Roberts and Systems.2003. P2143-2148.
[47]于洋.CCD数据采集系统及其CDS降噪技术的研究[D].燕山大学硕士论文.2004：P17-18.
[48]G.Yang, B.J.Nelson. Wavelet-based auto-focusing and unsupervised segmentation of microscopic images [C]. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems.2003:P2143-2148.
[49]Li F, Jin H. A Fast Auto-focusing method for digital still camera [A]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics [C]. Guangzhou, IEEE,2005:P5001-5005.
[50]Yang Boxiong, Hu Xinhe, Fu Huiqing. Noise Analysis and Processing of CCD Working Signal[J]. Optics & Optoelectronic Technology,2004:P51-53.
[51]Tobi Delbruck. Silicon retina for autofocus [A]. Circuits and Systems, ISCAS2000 Geneva,2000, Vol.4:P393-396.
[52]Muhammad Bilal Ahmad, Tae-Sun Choi. A Heuristic Approach for Finding Best Focused Shape [J]. IEEE Transaction on circuits and systems for video technology, 2005, Vol.15(4):P566-574.
[53]郑玉珍.自动聚焦中的优化爬山搜索算法[J].浙江科技学院学报,2005，Vo1.17(3)：P171-174.
[2]卜坤亮，王建东,樊玮虹等.基于FPGA芯片的超声波红外线测距系统[J].微处理机,2006,Vol.2：P88-90.
[3]Subbarao, Nikzad. Focusing techniques [J]. Opt. Eng,1993.
[4]黄剑琪.基于频谱分析的数字对焦技术研究[D].杭州：浙江大学硕士学位论文,2001：P4-6.
[5]原育凯.光学系统的自动调焦方法[J].红外与激光工程,2004：P15-21.
[6]Tse-Chtmg Wei, Murali Slabbarao. Continuous focusing of objects using DFDIS [J].2008, Vol.2183.
[7]王立强,林斌,陆祖康等.基于USB Video Camera的自动对焦系统[J].光电工程,2001,Vol.28(5)：P32-34.
[8]I.Raveh, D.Mendlovic, Z.Zatevsky, A.W.Lohraann. Digital method for defocus corrections:experimental results [J]. Optical Engineering,1999, Vol.38(10): P116-119.
[9]E.Krotkov. Focusing [J], International Journal of Computer Vision.1987, Vol.1: P223-237.
[10]Jie He, Rongzhen Zhou, Zhiliang Hong. Modified Fast Climbing Search Auto-focus Algorithm with Adaptive Step Size Searching Technique for Digital Camera[J]. IEEE Transactions on Consumer Electronics,2003, Vol.49(2): P257-262.
[11]徐洲.一种基于图像处理的自动聚焦方法的研究[D].中国科学院西安光学精密机械研究所,2003.
[12]张乐,姜威,高赞.数字图像一阶矩的自动聚焦区域选择算法[J].光学技术,2008,Vol.34(2)：P163-169.
[13]Kong-feng Zhu,Wei Jiang. Using Non-uniform Sampling to Make Automatic Focusing Result Correct [C]. IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications Proceedings,2005:P28-31.
[14]王倩，宋恩民，许向阳等.基于加权邻域相关性的显微镜自动聚焦函数[J].光学精密工程,2008,Vo1.29(1)：P53-55.
[15]蒋海华.基于图像清晰度评价函数的显微镜自动调焦技术研究[J].光学技术.2008，Vo1.34：P284-285.
[16]白磊.CCD显微测量系统自动调焦技术的研究[D].哈尔滨工业大学硕士学位论文,2002.
[17]Mason DC,Green DK. Automated Focusing of a Computer Controlled Microscope [C]. IEEE Trans Biomed Eng BME,2010, Vol.22:P137-150.
[18]Wang G, LChen D W, Tao CD. Research on Auto-focusing system Based on Image Processing[J]. SPIE,2008, Vol.3650:P88-92.
[19]Je-Ho Lee, Kun-Sop Kim, Bung-Deok Nam. Implementation of a Passive Automatic Focusing Algorithm for Digital Still Camera[C]. IEEE Transactions Consumer Electronics,2005, Vol.43(3):P421-429.
[20]Wang G, LChen D W, Tao CD. Research on Auto-focusing system Based on Image Processing[C]. SPIE.2006, Vol.3650:P88-92.
[21]夏德深,傅德盛.现代图像处理技术与应用.南京：东南大学出版社，1997.
[22]Shree K. Nayar. Yasuo Nakagawa. Shape from Focus[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence.1994, Vol.16(8):P824-831.
[23]朱孔凤.姜威.高赞.自动聚焦系统中聚焦窗口的选择及参量的确定[J].光学学报,2006.Vo1.26(6)：P777-780.
[24]Won C S, Pyan K. Automatic object segmentation in images with low depth of field [C]. Proc IEEE Int Conf on Image Proc.2002:P805-808.
[25]Yao Y, Abidi B, Doggaz N. Evaluation of sharpness measures and search algorithms for the auto-focusing of high magnification images[J]. Visual Information Processing,2006, Vol.62(46):P1-12.
[26]J J Liang, AK Qin, P N Suganthan, S Baskar. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multi-modal Functions [J]. IEEE Transactions on Evolutionary Computation Science (S1089-778X),2006, Vol.10(3): P281-295.
[27]高尚,杨静宇.群智能算法及其应用[M].北京：中国水利水电出版社,2006：P2-17.
[28]Eberhert R, Kennedy J. A New Optimizer Using Particle Swarm Theory [C]. Proc 6 Intl, Piscataway, Symposium on Micro Machine and Human Science, IEEE Service Center,1995:P1942-1948.
[29]Nanbo Jin, Yahya Rahmat. Advances in Particle Swarm Optimization for Antenna Designs:Real-Number, Binary, Single-Objective and Multi-objective Implementations [J]. IEEE Transactions on Antennas and Propagation. Science (S0018-926X),2007, Vol.53(3):P556-567.
[30]Said M Mikki, Ahmed A Kishk. Quantum Particle Swarm Optimization for Electromagnetic[J]. IEEE Transactions on antennas and propagation. Science (S0018-926X),2006. Vol.54(10):P2764-2775.
[31]李奇,徐之海.数字成像系统自动对区域设计[J].光子学报,2003,Vol.32(12)：P1499-1501.
[32]姜志国,韩冬兵,袁天云.基于全自动控制显微镜的自动聚焦算法研究[J].中国图形图像学报,2004,Vo1.9(4)：P397-401.
[33]P.T. Yap. P. Raveendran. Image focus measure based on Chebyshev moments[J]. IEEE Proc.-Vis. Image Signal Process,2004, Vol.15(2):P128-136.
[34]N.Kehtarnavaz, H.J.Oh. Development and real-time implementation of a rule-based autofocus algorithm [J]. Real-Time Imaging,2003. Vol.9:P197-203.
[35]M. Subbarao, T.S. Choi. A. Nikzad. Focusing techniques [J]. Optical Engineering, 1993. Vol.32:P 2824-2836.
[36]YOON K K, JUNE S L. ALDO W M. A video camera system with enhanced zoom tracking and auto white balance [J]. IEEE Transactions on Consumer Electronics, 2002,Vol.48(3):P428-434.
[37]周贤,姜威.基于图边缘能量的自动聚焦算法[J].光学技术,2006，Vol.32(2)：P213-215.
[38]Yi Yao, Besma Abidi, Narjes Doggaz. Evaluation of Sharpness Measures and Search Algorithms for the Auto- focusing of High Magnification Images[J]. Visual Information Processing,2006, Vol.(6246):P1212-1217.
[39]L. Firestone, K. Cook, K. Culp. Comparison of autofocus methods for automated microscopy [J].1991, Vol.12:P195-206.
[40]Lee SY, Kumar Y, Cho JM, Kim SW. Low-power passive focus measure operator based on the DCT for mobile phones[J]. The Imaging Science Journal,2008. Vol.56(6):P301-306.
[41]郑玉珍,吴勇.倪旭翔.实时自动对焦的研究[J].光电工程.2004,Vol.31(4)：P64-66.
[42]于保军,杨志刚,田丰君.宏/微双驱动定位台显微视觉自动聚焦清晰度评价方法[J].农业机械学报,2005，Vo1.6(4)：P552-555.
[43]李明明，王新赛.李坚.基于图像处理的自动聚焦清晰度函数比较研究.第九届全国光电技术学术交流会,2007,Vol.12(5)：P537-542.
[44]郭丙华,廖启亮,余志.基于小波变换的快速自动聚焦算法[J].中山大学学报(自然科学版).2007,Vo1.46(2)：P12-15.
[45]陈晓彬.基于小波的自动聚焦算法研究[J].通信技术,2009,Vo1.43(6)：P210-212.
[46]Yang G, Nelson BJ. Wavelet-Based autofocus- using and unsupervised segmentation of microscope images[C]. Proc. of the 2003 IEEE/RSJ Intl.Conference on Intelligence Roberts and Systems.2003. P2143-2148.
[47]于洋.CCD数据采集系统及其CDS降噪技术的研究[D].燕山大学硕士论文.2004：P17-18.
[48]G.Yang, B.J.Nelson. Wavelet-based auto-focusing and unsupervised segmentation of microscopic images [C]. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems.2003:P2143-2148.
[49]Li F, Jin H. A Fast Auto-focusing method for digital still camera [A]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics [C]. Guangzhou, IEEE,2005:P5001-5005.
[50]Yang Boxiong, Hu Xinhe, Fu Huiqing. Noise Analysis and Processing of CCD Working Signal[J]. Optics & Optoelectronic Technology,2004:P51-53.
[51]Tobi Delbruck. Silicon retina for autofocus [A]. Circuits and Systems, ISCAS2000 Geneva,2000, Vol.4:P393-396.
[52]Muhammad Bilal Ahmad, Tae-Sun Choi. A Heuristic Approach for Finding Best Focused Shape [J]. IEEE Transaction on circuits and systems for video technology, 2005, Vol.15(4):P566-574.
[53]郑玉珍.自动聚焦中的优化爬山搜索算法[J].浙江科技学院学报,2005，Vo1.17(3)：P171-174.