基于数字图像处理的自动调焦算法研究
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摘要
自动调焦技术是计算机视觉、成像系统和各种精密仪器中的关键技术之一。随着数字成像器件的实用化,近十年来人们对自动调焦技术的研究集中在数字图像处理的方法上,并取得了大量成果。基于图像技术的自动调焦方法采用了与传统调焦技术完全不同的方式进行调焦,传统的调焦方法是通过传感器检测检点或者测量距离的方式实现的,而基于图像技术的调焦方法直接根据图像分析出图像的质量,从而获得当前的成像状态,然后完成调焦操作。图像质量分析是该调焦方法中关键的技术。
本文首先对数字成像系统的自动调焦的原理和研究现状进行了简单的介绍,在此基础上,详细介绍了图像质量评价的过去与现状,然后,本文从两种途径详细论述了图像质量分析方法的实现:
(1)基于灰度子图的图像质量评价,提出了一种基于灰度区域搜索、对比的评价方法。将图像视为由具有不同灰度的区域组成的画面,寻找原始图像中每个灰度子图的失真,比较每个灰度子图在原始图像与退化图像中的变化,进而评价整幅图像的质量的退化。
(2)基于小波分析的图像质量评价,以小波分析的方法对图像进行多分辨率分解,对各小波分量做统计处理,分析了图像质量变化体现在小波能量谱上的变化。通过分析小波系数的变化,可以确定图像质量的优劣。
调焦的实现对于对焦深度法是计算每次成像的调焦函数值,结合一维搜索方法、Fibonacci搜索法、函数逼近法或爬山法,不断逼近正焦位置;对于离焦深度法是根据评价出的图像质量和已知的系统参数,确切地改变焦距调整量。在自动调焦理论的基础上提出了一种采用DSP(Digital SignalProcessor)+FPGA(Field Programmable Gate Array)的自动调焦系统的架构,并分析了系统的总体性能。
Auto-focus is one of the key problems in computer vision, imaging systems and various precision instruments. In the last ten years, research on auto-focusing techniques has been focused on methods related to digital image processing on account of the maturing of digital imaging devices, and a lot of achievements have been accomplished. The auto-focus means based on image technology is different from those traditional auto-focus means entirely. The traditional ones must depend on some special assistant equipment. They use these equipments to measure distance or find focus and realize auto-focus. However, the auto-focus based on image technology is completed by image quality analysis. It can judge the imaging state from the current image quality, and then the system can adjust focus distance correctly supervised by imaging state. The image quality analysis is the key technology.
First, the dissertation briefs the principles and research status of auto-focus in digital imaging systems. Then, the dissertation discussed the past and present state of image quality evaluation. Following that, two image quality analysis methods are presented in detail in the thesis:
(1) One is the assessment of image quality through gray scale area. It describes a new method which evaluates quality of image through searching and contrasting gray scale area. In this method, image was treated as being composed of many different gray scale areas. Distortion of each gray scale area was computed. Then the image quality was assessed and the simulation results are presented. And it indicates that this algorithm works effectively.
(2) The other one is the wavelet based image quality evaluation. It analyzes image from different frequency resolutions through wavelet theory, and analyzes the change of Wavelet Coefficients through statistics analysis, and then assesses the image quality.
The realization of auto-focus, for the DFF (depth from focus), is similar with the 1-demension search, Fibonacci search, function approaching or MCS (mountain climbing servo), calculates the auto-focus function value, compares it with the previous one and decides the next position; for the DFD (depth from defocus), moves a special distance relative to the image quality level and system parameters directly. Depending on above auto-focus theories, the thesis puts forward an auto-focus system architecture and analyzes its performance, which is constructed by high speed DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array).
本文首先对数字成像系统的自动调焦的原理和研究现状进行了简单的介绍,在此基础上,详细介绍了图像质量评价的过去与现状,然后,本文从两种途径详细论述了图像质量分析方法的实现:
(1)基于灰度子图的图像质量评价,提出了一种基于灰度区域搜索、对比的评价方法。将图像视为由具有不同灰度的区域组成的画面,寻找原始图像中每个灰度子图的失真,比较每个灰度子图在原始图像与退化图像中的变化,进而评价整幅图像的质量的退化。
(2)基于小波分析的图像质量评价,以小波分析的方法对图像进行多分辨率分解,对各小波分量做统计处理,分析了图像质量变化体现在小波能量谱上的变化。通过分析小波系数的变化,可以确定图像质量的优劣。
调焦的实现对于对焦深度法是计算每次成像的调焦函数值,结合一维搜索方法、Fibonacci搜索法、函数逼近法或爬山法,不断逼近正焦位置;对于离焦深度法是根据评价出的图像质量和已知的系统参数,确切地改变焦距调整量。在自动调焦理论的基础上提出了一种采用DSP(Digital SignalProcessor)+FPGA(Field Programmable Gate Array)的自动调焦系统的架构,并分析了系统的总体性能。
Auto-focus is one of the key problems in computer vision, imaging systems and various precision instruments. In the last ten years, research on auto-focusing techniques has been focused on methods related to digital image processing on account of the maturing of digital imaging devices, and a lot of achievements have been accomplished. The auto-focus means based on image technology is different from those traditional auto-focus means entirely. The traditional ones must depend on some special assistant equipment. They use these equipments to measure distance or find focus and realize auto-focus. However, the auto-focus based on image technology is completed by image quality analysis. It can judge the imaging state from the current image quality, and then the system can adjust focus distance correctly supervised by imaging state. The image quality analysis is the key technology.
First, the dissertation briefs the principles and research status of auto-focus in digital imaging systems. Then, the dissertation discussed the past and present state of image quality evaluation. Following that, two image quality analysis methods are presented in detail in the thesis:
(1) One is the assessment of image quality through gray scale area. It describes a new method which evaluates quality of image through searching and contrasting gray scale area. In this method, image was treated as being composed of many different gray scale areas. Distortion of each gray scale area was computed. Then the image quality was assessed and the simulation results are presented. And it indicates that this algorithm works effectively.
(2) The other one is the wavelet based image quality evaluation. It analyzes image from different frequency resolutions through wavelet theory, and analyzes the change of Wavelet Coefficients through statistics analysis, and then assesses the image quality.
The realization of auto-focus, for the DFF (depth from focus), is similar with the 1-demension search, Fibonacci search, function approaching or MCS (mountain climbing servo), calculates the auto-focus function value, compares it with the previous one and decides the next position; for the DFD (depth from defocus), moves a special distance relative to the image quality level and system parameters directly. Depending on above auto-focus theories, the thesis puts forward an auto-focus system architecture and analyzes its performance, which is constructed by high speed DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array).
引文
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[2]董太和.相机的MF/AF机构及精度[J].照相机,2000(02):33-36.
[3]黄其昆.自动调焦二十年[J].大众摄影,2001(05):52-55.
[4]杜锴.诺日士中国市场营销策略研究[D].北京:对外经济贸易大学(硕士),2006:1-2.
[5]CHAN W H,YOUE C T.Video CCD based portable digital still camera [J].IEEE Transactions on Consumer Electronics,1995,41(3):455-459.
[6]ZHANG X,FUKUDA N,OBUCHI Y,et al.A signal processing system on chip for digital cameras[C]//26~(th) Annual Conference of the IEEE.Anchorage,Alaska,USA:IEEE,2000,2:1243-1248.
[7]YOON K,KIM C,LEE B,et al.Single-chip CMOS image sensor for mobile applications[C]//Solid-State Circuits Conference.Florence:IEEE,2002,1:36-44.
[8]GETZLAFL S,SCHREITER J,GRAUPNER A.A system-on-chip realization of a CMOS image sensor with programmable analog image preprocessing[C]//The 2001 IEEE International Symposium on Circuits and Systems.Newport Beach:IEEE,2001,4:486-489.
[9]梁翠萍,李清安,乔彦峰,等.简析光学系统自动调焦的方法[J].电光与控制,2006,13(06):93-96.
[10]原育凯.光学系统的自动调焦方法[J].红外,2004(06):15-21.
[11]田宜彬,徐之海,冯华君.基于数字图像处理的自动对焦技术[C]//自动化与信息技术发展战略研讨会(CASIA CAIT2001)论文集.北京:中国科学院自动化研究所,2001:388-393.
[12]SUBBARAO M,NIKZAD A,Focusing techniques[J].Optical Engineering,1993,32(11):2824-2836.
[13]STOKSETH P A.Properties of a Defocused optical system[J].Journal of the Optical Society of America,1969,59(10):1314-1321.
[14]许盛,李见为.基于智能化自动调焦的高级显微镜系统[J].光电工程,2000,27(01):33-36.
[15]王建民.二维图像测量机及图像式大尺寸弧长在线测量系统的研究[D].哈尔滨:哈尔滨工业大学(硕士),1998:3-5.
[16]SUBBARAO M,LIU YEN FU.Accurate reconstruction of 3-D shape and focused image from a sequence of noisy defocused images[J].SPIE,1997,2909:178-189.
[17]RAVEH I,MENDLOVIC D,ZALEVSKEY Z,et al.Digital method for defocus corrections:experimental results[J].Optical Engineering,1999,38(10):1620-1626.
[18]KIM K S,PARK R S,PAIK K J.Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system[J].IEEE Transactions on Consumer Electronics,1998,44(3):1071-1075.
[19]KIM K S,KIM K T,PAIK K J.A fully digital auto-focusing system based on image restoration[C]//Proceeding of IEEE Region 10 Annual Conference on Speech and Image Technologies for Computing and Telecommunications.Brisbane,Australia:IEEE,1997,1:13-16.
[20]SUBBARAO M,GURUMOORTHY N.Depth recovery from blurred edges [C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,D.C.:IEEE Computer Society Press,1988,498-503.
[21]裴锡宇,冯华君,李奇,等.一种基于频谱分析的离焦深度自动对焦法[J].光电工程,2003,30(5):62-65.
[22]MURALI.Parallel depth recovery by changing camera parameters [C]//Second International Conference on Computer Vision.New York:IEEE Computer Society Press,1988:149-155.
[23]AHMET M E,PAUL S F.Image Quality Measures and Their Performance [J].IEEE Transactions on Communications,1995,43(12):2959-2965.
[24]DUSEK J,ROUBIK K.Testing of new model of the human visual system for image quality evaluation[C]//Seventh International Symposium on Signal Processing and Its Application.Paris:IEEE,2003:621-622.
[25]ALEKSANDR SHNAYDERMAN,ALEXANDER GUSEV,AHMET M.E.An SVD-Based Gray-Scale Image Quality Measure for Local and Global Assessment[J].IEEE Transactions on Image Process,2005,14(2):422-429.
[26]YAO HONG XUN,HU MIN YU,YAO GUI LIN.Image Evaluation Factors[C]//Lecture Notes in Computer Science.Berlin/Heidelberg:Springer,2005:255-262.
[27]徐鲁安,叶懋冬,章琦.一种新的图像质量评价方法[J].计算机工程与设计,2004,25(3):418-420.
[28]熊兴华.数字影像质量评价方法评述[J].测绘科学,2004,29(1):68-71.
[29]李奇,冯华君,徐之海,等.数字图象清晰度评价函数研究[J].光子学报,2002,31(6):736-738.
[30]李天柁,弟羽鸣,肖刚,等.核径迹图像自动分析系统自动聚焦方法研究[C]//第九届全国核电子学与核探测技术学术年会论文集.大连:中国电子学会,1998:354-356.
[31]白立芬,徐毓娴,于水等,基于图像处理的显微镜自动调焦方法研究[J].仪器仪表学报,1999,20(6):612-614.
[32]白立芬,于水,薛实福等,基于图像处理的自动调焦方法中清晰度判据函数的研究[J].仪器仪表学报,1999,20(s4):85-86.
[33]GROSSMAN A,MORLET J.Decomposition of Hardy Function into Square Integrable Wavelets of Constant Shape[J].SIAM J.Math.Anal,1984,15(4):723-736.
[34]成礼智,郭汉伟.小波与离散变换理论及工程实践[M].北京:清华大学出版社,2005:72-96.
[35]MALLAT.信号处理的小波导引[M].杨力华等译.北京:机械工业出版社,2002:86-105.
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