基于图像技术的自动调焦方法研究与实现
|
摘要
自动调焦已成为各种成像系统的重要功能，与国外相比，国内在这方面的工作做得还比较少，同时图像处理技术的发展使得自动调焦趋于数字化和智能化，提出基于图像技术的自动调焦方法具有重要的实际意义。
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焦点或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的关键技术，本文从三种途经详细论述了图像质量分析方法的实现：
(1) 基于对比度的图像质量分析方法从图像的时域、频域及信息熵三个角度建立能表示图像对比度的一些调焦函数，并对这些评价函数做了详细的比较，最后确定出时域的Brenner函数和绝对方差函数具有更好的综合性能；
(2) 基于功率谱的客观图像质量分析方法假设场景的功率谱具有不变特性，引入了人类视觉系统，加入了维纳噪声滤波器，对图像质量进行评价可得到一个确定的IOM数值，该数值与人的视觉评价具有很高的相关性；
(3) 基于小波与神经网络的图像质量分析方法利用小波分析对图像进行多分辨率分解，分析其细节信息并采用统计的方法提取图像特征，再利用人工神经网络对图像特征进行质量模式识别，得到图像的质量等级，实验表明，该方法达到了不错的识别率。
调焦的实现对于对比度法是计算每次成像的调焦函数值，结合一维搜索方法，不断逼近正焦位置；对于功率谱方法和小波与神经网络方法是根据评价出的图像质量，确切地改变焦距调整量。在自动调焦理论的基础上提出了自动调焦系统的设计，分析了系统的总体性能并作为软硬件设计的依据，调焦系统采用DSP+FPGA的高速硬件系统方案。
Auto-focus has being an important function in all kind of imaging systems, it has widely application in many fields. In our country, the research and development about auto-focus is poor, but we are facing kinds of application requirement. The image processing is a technology developing rapidly, it promotes the development of auto-focus technology greatly, and numeralization and intelligentization has being a tendence of auto-focus technology. For these conditions, providing an auto-focus means based on image processing technology will have important significance.
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 equipments, they uses 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, then the system can adjust focus distance correctly supervised by imaging state. From the work flow, we know the image quality analysis is the key technology. In the thesis, three image quality analysis methods are presented detailedly:
(1) The first one is the image quality analysis based on image contrast, it analyzes kinds of functions working at temporal field, frequency field and information entropy, compares their performance in the number of calculation and the time consumption of calculation, and finds out two better functions.
(2) The next one is the image quality analysis based on image power spectrum, it supposes nature scenes have the same power spectrum, embeds a human vision system and includes a Winner de-noise filter. It analyzes a picture and gets an IQM (Image Quality Measure) value, the IQM value is highly relative to the estimation result through human vision system.
(3) The last one is the image quality analysis based on wavelet analysis and neural network, it analyzes image from different frequency resolution through wavelet theory, and abstract the image characteristics from these detail information through statistics analysis, then uses an artificial neural network for quality pattern recognition. The recognition rate of this means is good.
The realization of auto-focus, for contrast means, is similar with the 1-demension search, calculates the auto-focus function value, compares it with the previous one and decides the next position; for power spectrum or wavelet means, moves a special distance relative to the image quality level directly.
Depending on above auto-focus theories, design an auto-focus system, analyze its performance and present the design of hardware and software particularly, the system is constructed by high speed DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array).
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焦点或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的关键技术，本文从三种途经详细论述了图像质量分析方法的实现：
(1) 基于对比度的图像质量分析方法从图像的时域、频域及信息熵三个角度建立能表示图像对比度的一些调焦函数，并对这些评价函数做了详细的比较，最后确定出时域的Brenner函数和绝对方差函数具有更好的综合性能；
(2) 基于功率谱的客观图像质量分析方法假设场景的功率谱具有不变特性，引入了人类视觉系统，加入了维纳噪声滤波器，对图像质量进行评价可得到一个确定的IOM数值，该数值与人的视觉评价具有很高的相关性；
(3) 基于小波与神经网络的图像质量分析方法利用小波分析对图像进行多分辨率分解，分析其细节信息并采用统计的方法提取图像特征，再利用人工神经网络对图像特征进行质量模式识别，得到图像的质量等级，实验表明，该方法达到了不错的识别率。
调焦的实现对于对比度法是计算每次成像的调焦函数值，结合一维搜索方法，不断逼近正焦位置；对于功率谱方法和小波与神经网络方法是根据评价出的图像质量，确切地改变焦距调整量。在自动调焦理论的基础上提出了自动调焦系统的设计，分析了系统的总体性能并作为软硬件设计的依据，调焦系统采用DSP+FPGA的高速硬件系统方案。
Auto-focus has being an important function in all kind of imaging systems, it has widely application in many fields. In our country, the research and development about auto-focus is poor, but we are facing kinds of application requirement. The image processing is a technology developing rapidly, it promotes the development of auto-focus technology greatly, and numeralization and intelligentization has being a tendence of auto-focus technology. For these conditions, providing an auto-focus means based on image processing technology will have important significance.
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 equipments, they uses 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, then the system can adjust focus distance correctly supervised by imaging state. From the work flow, we know the image quality analysis is the key technology. In the thesis, three image quality analysis methods are presented detailedly:
(1) The first one is the image quality analysis based on image contrast, it analyzes kinds of functions working at temporal field, frequency field and information entropy, compares their performance in the number of calculation and the time consumption of calculation, and finds out two better functions.
(2) The next one is the image quality analysis based on image power spectrum, it supposes nature scenes have the same power spectrum, embeds a human vision system and includes a Winner de-noise filter. It analyzes a picture and gets an IQM (Image Quality Measure) value, the IQM value is highly relative to the estimation result through human vision system.
(3) The last one is the image quality analysis based on wavelet analysis and neural network, it analyzes image from different frequency resolution through wavelet theory, and abstract the image characteristics from these detail information through statistics analysis, then uses an artificial neural network for quality pattern recognition. The recognition rate of this means is good.
The realization of auto-focus, for contrast means, is similar with the 1-demension search, calculates the auto-focus function value, compares it with the previous one and decides the next position; for power spectrum or wavelet means, moves a special distance relative to the image quality level directly.
Depending on above auto-focus theories, design an auto-focus system, analyze its performance and present the design of hardware and software particularly, the system is constructed by high speed DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array).
引文
1．王任华，沈忙作．自动调焦算法研究．光电工程，2000，27(4)：11-13．
2．许盛，李见为．高级显微镜系统智能化自动调焦的研究及实现．光学仪器，2000，22(5)：22-26．
3．黄其昆．自动调焦二十年．大众摄影，2001，(5)：52-55．
4．一川．中国应开发AF相机．照相机，2000，(2)：8-10．
5．杨成军，林国辉，全子一．压缩图像清晰度的增强研究．电视技术，2000，(2)：6-8．
6．董太和．相机的MF／AF机构及其精度：相机机构专题讲座(1)．照相机，2000，(2)．
7．王建民，浦昭邦，尹继学，晏磊．图像式显微自动调焦瞄准系统的研究．光学技术，2000，26(5)：410-417．
8．周曲，吴黎明，唐露新，王桂棠．基于自动调焦的微机控制显微硬度计．广东自动化与信息工程．2001(4)：30-33．
9．郭彦珍，邱宗明，李信，王吉晖．图像测量技术中一种调焦的判别方法．西安理工大学学报，2001，17(1)：40-42．
10．许盛，李见为．基于智能化自动调焦的高级显微镜系统．光电工程，2000，27(1)：33-36．
11．李开端，李英杰，赵育良，张青臣．基于面阵CCD和小波变换的航空相机自动调焦研究光学技术，2002，28(2)：150-154．
12．李开端，赵育良，李英杰，张青臣．基于面阵CCD的相机自动调焦技术．传感器技术，2002，21(6)：46-49．
13．李开端，李英杰，赵育良，张青臣．基于多尺度分析的航空相机自动调焦系统研究．航空计测技术，2001，21(6)：8-10．
14．许兆林，付战平．航空CCD侦察相机系统研究．航空计测技术．2001，21(5)：3-6．
15．李桂苓，张浩，王楠楠，郝铁乱．提高数字图像清晰度的研究．信号处理，2000，16(增刊)：36-40．
16．李桂苓，王楠楠，徐岩．数字运动图像清晰度及其测试．电子科技大学学报，2001，30(4)：359-362．
17．李奇，冯华君，徐之海，边美娟，申溯，戴瑞春．数字图像清晰度评价函数研究．光子学报，2002，31(6)：736-738．
18．曹念文，刘文清，张玉钧，王锋平，宋炳超．偏振成像清晰度与成像距离关系的讨论．光学学报，2000，20(8)：1145-1147．
19．李桂苓，李伯桦，王金健．数字图像清晰度．电视技术，1999，(4)：19-21．
20．吴世法，近代成像技术与图像处理．北京：国防工业出版社．1997．
21．沈庭芳，方子文编著．数字图像处理及模式识别．北京：北京理工大学出版社．1998．
22．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．
23．Kenneth. R. Castleman著，朱志刚等译．数字图像处理．北京：电子工业出版社，1998．
24．王润生编著．图像理解．长沙：国防科技大学出版社，1995．
25．杨长生编著．图像与声音压缩技术．杭州：浙江大学出版社．2000．
26．张兆礼，赵春晖，梅晓丹著．现代图像处理技术及Matlab实现．北京：人民邮电出版社．2001．
27．郑南宁著．计算机视觉与模式识别．北京：国防工业出版社，1998．
28．程佩青．数字信号处理教程．北京：清华大学出版社，1998．
29．邓先礼编著．最优化技术．重庆：重庆大学出版社，1998．
30．粟塔山等编著．最优化计算原理与算法程序设计．长沙：国防科技大学出版社，2001．
31．罗发龙，李衍达著．神经网络信号处理，北京：电子工业出版社，1993．
32．黄德双编著．神经网络模式识别系统理论．北京：电子工业出版社，1996．
33．焦李成编著．神经网络的应用与实现．西安：西安电子科技大学出版社，1996．
34．Abhiiit S. Pandya,Robert B．Macy著，徐勇，荆涛等译．神经网络模式识别及应用．北京：电子工业出版社，1999．
35．杨建刚编著．人工神经网络实用教程．杭州：浙江大学出版社，2002．
36．王志宏，徐建生．BP神经网络结构设计的频谱取值模型研究．武汉化工学院学报，2001，23(9)：63-65．
37．边肇祺，张学工等编著．模式识别(第二版)．北京：清华大学出版社，2000．
38．程正兴著．小波分析算法与应用．西安：西安交通大学出版社，1998．
39．王建平，盛军，朱程辉．基于小波分析的视频图像字符特征提取方法研究．微电子与计算机，2002，5：51-56．
40．杨胜波，于春海．小波分析在生物医学信号／图像处理中的应用．仪器仪表学报，2002，23(3)增刊：179-181．
41．孟军，魏同立，吴金，常昌远．数字图像离散小波变换的原理与硬件实现分析．东南大学学报(自然科学版)，2002，32(6)：1-6．
42．韦志辉，程军．基于小波变换的一种新的图像质量评价方法．南京理工大学学报，1998，22(6)：552-555．
43．甘斌，张雄伟，甘仲民．基于小波变换的多尺度图像边缘处理．数字电视与数字视频，2001．8：20-22．
44．黄应清，季向琦，张智诠．图像小波系数的分布规律及其对图像质量的影响．红外与激光工程，2000，29(3)：15-18．
45．张新明，沈兰荪．基于小波和统计特性的自适应图像增强．信号处理，2001，17(3)：227-231．
46．鞠彦忠，阎贵平，陈建斌，陈景彦，陈建华．用小波神经网络检测结构损伤．工程力学，2003，20(6)：176-181．
47．张雄伟，曹铁勇编著．DSP芯片的原理与开发应用(第二版)．北京：电子工业出版社．2001．
48．申敏，邓矣兵等编著．DSP原理及其在移动通信中的应用．北京：人民邮电出版社．2001．
49．Uwe．Meyer-Baese著，刘凌，胡永生译．数字信号处理的FPGA实现．北京：清华大学出版社，2003．
50．《中国集成电路大全》编委会编著．专用集成电路和集成系统自动化设计方法．北京：国防工业出版社，1997．
51．潘松，王国栋编著．VHDL实用教程．成都：电子科技大学出版社，2000．
52．袁祥辉．固体图像传感器及其应用．重庆：重庆大学出版社，1996．
53．盛骤，谢式千，潘承毅编．概率论与数理统计(第二版)．北京：高等教育出版社，2000．
54. TMS320VC5402 Fixed-Point Digital Signal Processor. TI, 2000.
55. TMS320C54x DSP Reference Set Volume 1: CPU and Peripherals. TI, 2001.
56. TMS320C54x DSP Reference Set Volume 2: Mnemonic instruction Set. TI, 2001.
57. TMS320C54x DSP Reference Set Volume 3: Algebraic Instruction Set. TI, 2001.
58. TMS320C54x DSP Reference Set Volume 4: Applications Guide.TI, 1996.
59. TMS320C54x DSP Reference Set Volume 5: Enhance Peripherals. TI, 1999.
60. TMS320VC5402 and TMS320UC5402 Bootloader. TI, 2002.
61. Spartan-Ⅱ 2.5V FPGA Family: Introduction and Ordering Information. Xilinx, Inc., 2001.
62. Spartan-Ⅱ 2.5V FPGA Family: Functional Description. Xilinx, Inc., 2001.
63. Spartan-Ⅱ 2.5V FPGA Family: DC and Switching Characteristics. Xilinx, Inc., 2002.
64. Spartan-Ⅱ 2.5V FPGA Family: Pinout Tables. Xilinx, Inc., 2001.
65. Configuring FPGAs Over a Processor Bus. Xilinx, Inc., 1995.
66. ISO/IEC JTCI/SC29 WG1, JPEG 2000 Part Ⅰ Final Committee Draft Version 1.0. 2000.
67. Cheol-Hee Park, Jong-Ho Paik, Young-Hwan You, Hyoung-Kyu Song, Yong-Soo Cho, Auto focus filter design and implementation using correlation between filter and auto focus criterion. International Conference on ICCE 2000, 2000:250-251.
68. Dickson, S. D., Tyminski, J. K., McNamara, S. J., Humphrey, D. C., Ziemer, D., FC2: focus control technique for critical level i-line photolithography. Advanced Semiconductor Manufacturing Conference and Workshop, 1997,10(12): 309-316.
69. Moreira, A., Mittermayer, J., Scheiber, R., A SAR auto-focus technique based on azimuth scaling. Geoscience and Remote Sensing, 1997, 4: 2028-2030.
70. Komatsu, F., Motaki, H., Miyoshi, M., A new auto-focus method in critical dimension measurement SEM. Test Symposium, 1997, 17(19): 202-207.
71. Soatto, S., Observability of shape from focus. Decision and Control, 1998, 3:3251-3256.
72. Yoshida, Y., Shinohara, S., Ikeda, H., Tada, K., Yoshida, H., Nishide, K., Sumi, M., Control of lens position in auto-focus cameras using semiconductor laser range finder. Circuits and Systems, 1991, 1: 395-398.
73. E. Hughlett and P. Kaiser, An autofocus technique for imaging microscopy. ICASSP-92, 1992, 3: 93-96.
74. Wen-Hsin Chan, Ching-Twu Youe, Video CCD based portable digital still camera. IEEE Transactions on Consumer Electronics, 1995, 41 (3): 455-459.
75. Koizumi, T., Hwan-Sul Chun, Zen, H., A new optical detector for a high-speed AF control. IEEE Transactions on Consumer Electronics, 1996, 42(4): 1055-1061.
76. Ooi, K., Izumi, K., Nozaki, M., Takeda, I., An Advanced Auto-Focus System for Video Camera Using Quasi Condition Reasoning. IEEE 1990 International Conference on Consumer Electronics, 1990: 348-349.
77. Zhang, X., Fukuda, N., Obuchi, Y., Kambe, T., Kubo, N., Kawamura, H., Suzuki, I., A signal processing system on chip for digital cameras. 26th Annual Conference of the IEEE, 2000, 2: 1243-1248.
78. Kwangho Yoon, Chanki Kim, Bumha Lee, Doyoung Lee, Single-chip CMOS image sensor for mobile applications. Solid-State Circuits Conference, 2002, 1: 36-442.
79. Getzlaff, S., Schreiter, J., Graupner, A., Schuffny, R., A system-on-chip realization of a CMOS image sensor with programmable analog image preprocessing. The 2001 IEEE International Symposium on Circuits and Systems, 2001,4: 486-489.
80. Tabet, M., Hornsey, R., CMOS image sensor camera with focal plane edge detection. Canadian Conference on Electrical and Computer Engineering, 2001, 2:1129-1133.
81. Nair, R., Raj, K., Signal processing in CMOS image sensors. 2000 IEEE Workshop on Signal Processing Systems, 2000:801-810.
82. David Atkinson, Derek L. G. Hill, Peter N. R. Stoyle, Paul E. Summers, and Stephen F. Keevil, Automatic Correction of Motion Artifacts in Magnetic Resonance Images Using an Entropy Focus Criterion. IEEE Transactions on Medical Imaging, 1997,16(6): 903-910.
83. Li Xi, Liu Guosui, Jinlin Ni. Autofocusing of ISAR Images Based on Entropy Minimization.
IEEE Transactions on Aerospace and Electronic System, 1999,35(4): 1240-1252.
84. A.D. Brink. Using spatial information as an aid to maximum entropy image threshold selection. Pattern Recognition Letters, 1996,17: 29-36.
85. N.B. Nill and B. H. Bouzas, Objective Image Quality Measures Derived From Digital Image Power Spectra, Optical Engineering, 1992, 31 (4): 813-825.
86. K. Falconer, Fractal Geometry - Mathematical Foundations and Applications. Wiley, New York (1990).
87. Y. Itakura, S. Tsutsumi, and T. Takagi, Statistical properties of the background noise for the atmospheric windows in the intermediate infrared region. Infrared Physics, 1974, 14:17-29.
88. B. R. Hunt and T. M. Cannon, Nonstationary assumptions for gaussian models of images. IEEE Trans. Syst., 1976, 6 (12): 876-882.
89. D.J. Granrath, The role of human visual models in image processing. Proc. IEEE, 1981, 69 (5): 552-561.
90. N. B. Nill, A visual model weighted cosine transform for image compression and quality assessment. IEEE Trans. Commun. 1985, 33 (6): 551-557.
91. J.J. DePalma and E. M. Lowry, Sine wave response of the visual system. Ⅱ. Sine wave and square wave contrast sensitivity. J. Opt. Soc. Am, 1962, 52 (3): 328-335.
92. J.L. Mannos and D. J. Sakrison, The effects of a visual fidelity criterion on the encoding of images. IEEE Trans. Inform. Theory, 1974, 20 (4): 525-536.
93. K. N. Ngan, K. S. Leong, and H. Singh, Adaptive cosine transform coding of images in perceptual Domain. IEEE Trans. Acoust. Speech Signal Process. 1989, 37 (11): 1743-1750.
94. E.A. Fadhel and P. Bhattacharyya, Application of a Steerable Wavelet Transform using Neural Network for Signature Verification. Pattern Analysis & Applications, 1999, 2: 184-195.
95. B.J.W. Fleming, D. Yu, R.G. Harrison, and D. Jubb. Wavelet-based detection of coherent structures and self-affnity in financial data. Eur. Phys. J. B, 2001,(20): 543-546.
96. Z. Peng, Y. He, Q. Lu and F. Chu. Feature Extraction of the Rub-impact Rotor System By Means of Wavelet Analysis. Journal of Sound and Vibration, 2003,259(4): 1000-1010.
97. M. Antonopoulos-Domis, T. Tambouratzis. System Identification a Transient via Wavelet Multiresolution Analysis. Ann. Nucl. Energy, 1998, 25(6): 465-480.
98. I. Urriza, J. I. Artigas, L. A. Barragan, J. I. Garcia and D. Navarro. VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images. Real-Time Imaging, 2001, 7: 203-217.
99. L. Bruzzone, F. Roli and S.B. Serpico. Structure neural networks for signal classification. Signal Processing, 1998,64:271-290.
100. Steven K. Rogers, John M. Colombi, Curtis E. Martin, etc. Neural Networks for Automatic Target Recognition. Neural Networks, 1995,8(7): 1153-1184.
101. Krzysztof J. Cios, Inbo Shin. Image Recognition Neural Network: IRNN. Neurocomputing, 1995,7: 159-185.
102. A. Ravichandran, B. Yegnanarayana. Studies on Object Recognition From Degraded Images Using Neural Networks. Neural Networks, 1995,8(3): 481-488.
103. J. Antonio Catalan, J.S. Jin and T. Gedeon. Reducing the Dimensions of Texture Features for Image Retrieval Using Multilayer Neural Networks. Pattern Analysis & Applications, 1999,2: 196-203.
104. D. de Ridder, R.P.W. Duin, P.W. Verbeek and L.J. van Vliet. The Applicability of Neural Networks to Non-linear Image Processing. Pattern Analysis & Applications, 1999,2: 111-128.
105. A. Datta, S. Pal and S. Chakraborti. Image Thinning by Neural Networks. Neural Comput & Applications, 2002,11: 122-128.
106. S. Singh, A. Amin2. Neural Network Recognition of Hand-printed Characters. Neural Cornput & Applications, 1999,8: 67-76.
107. Mandana Ebadian Dehkordi, Nasser Sherkat, Tony Allen. Handwriting style classification. IJDAR, 2003,6: 55-74.
108. P.A. Fadhel and E Bhattacharyya. Application of a Steerable Wavelet Transform using Neural Network for Signature Verification. Pattern Analysis & Applications, 1999,2: 184-195.
2．许盛，李见为．高级显微镜系统智能化自动调焦的研究及实现．光学仪器，2000，22(5)：22-26．
3．黄其昆．自动调焦二十年．大众摄影，2001，(5)：52-55．
4．一川．中国应开发AF相机．照相机，2000，(2)：8-10．
5．杨成军，林国辉，全子一．压缩图像清晰度的增强研究．电视技术，2000，(2)：6-8．
6．董太和．相机的MF／AF机构及其精度：相机机构专题讲座(1)．照相机，2000，(2)．
7．王建民，浦昭邦，尹继学，晏磊．图像式显微自动调焦瞄准系统的研究．光学技术，2000，26(5)：410-417．
8．周曲，吴黎明，唐露新，王桂棠．基于自动调焦的微机控制显微硬度计．广东自动化与信息工程．2001(4)：30-33．
9．郭彦珍，邱宗明，李信，王吉晖．图像测量技术中一种调焦的判别方法．西安理工大学学报，2001，17(1)：40-42．
10．许盛，李见为．基于智能化自动调焦的高级显微镜系统．光电工程，2000，27(1)：33-36．
11．李开端，李英杰，赵育良，张青臣．基于面阵CCD和小波变换的航空相机自动调焦研究光学技术，2002，28(2)：150-154．
12．李开端，赵育良，李英杰，张青臣．基于面阵CCD的相机自动调焦技术．传感器技术，2002，21(6)：46-49．
13．李开端，李英杰，赵育良，张青臣．基于多尺度分析的航空相机自动调焦系统研究．航空计测技术，2001，21(6)：8-10．
14．许兆林，付战平．航空CCD侦察相机系统研究．航空计测技术．2001，21(5)：3-6．
15．李桂苓，张浩，王楠楠，郝铁乱．提高数字图像清晰度的研究．信号处理，2000，16(增刊)：36-40．
16．李桂苓，王楠楠，徐岩．数字运动图像清晰度及其测试．电子科技大学学报，2001，30(4)：359-362．
17．李奇，冯华君，徐之海，边美娟，申溯，戴瑞春．数字图像清晰度评价函数研究．光子学报，2002，31(6)：736-738．
18．曹念文，刘文清，张玉钧，王锋平，宋炳超．偏振成像清晰度与成像距离关系的讨论．光学学报，2000，20(8)：1145-1147．
19．李桂苓，李伯桦，王金健．数字图像清晰度．电视技术，1999，(4)：19-21．
20．吴世法，近代成像技术与图像处理．北京：国防工业出版社．1997．
21．沈庭芳，方子文编著．数字图像处理及模式识别．北京：北京理工大学出版社．1998．
22．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．
23．Kenneth. R. Castleman著，朱志刚等译．数字图像处理．北京：电子工业出版社，1998．
24．王润生编著．图像理解．长沙：国防科技大学出版社，1995．
25．杨长生编著．图像与声音压缩技术．杭州：浙江大学出版社．2000．
26．张兆礼，赵春晖，梅晓丹著．现代图像处理技术及Matlab实现．北京：人民邮电出版社．2001．
27．郑南宁著．计算机视觉与模式识别．北京：国防工业出版社，1998．
28．程佩青．数字信号处理教程．北京：清华大学出版社，1998．
29．邓先礼编著．最优化技术．重庆：重庆大学出版社，1998．
30．粟塔山等编著．最优化计算原理与算法程序设计．长沙：国防科技大学出版社，2001．
31．罗发龙，李衍达著．神经网络信号处理，北京：电子工业出版社，1993．
32．黄德双编著．神经网络模式识别系统理论．北京：电子工业出版社，1996．
33．焦李成编著．神经网络的应用与实现．西安：西安电子科技大学出版社，1996．
34．Abhiiit S. Pandya,Robert B．Macy著，徐勇，荆涛等译．神经网络模式识别及应用．北京：电子工业出版社，1999．
35．杨建刚编著．人工神经网络实用教程．杭州：浙江大学出版社，2002．
36．王志宏，徐建生．BP神经网络结构设计的频谱取值模型研究．武汉化工学院学报，2001，23(9)：63-65．
37．边肇祺，张学工等编著．模式识别(第二版)．北京：清华大学出版社，2000．
38．程正兴著．小波分析算法与应用．西安：西安交通大学出版社，1998．
39．王建平，盛军，朱程辉．基于小波分析的视频图像字符特征提取方法研究．微电子与计算机，2002，5：51-56．
40．杨胜波，于春海．小波分析在生物医学信号／图像处理中的应用．仪器仪表学报，2002，23(3)增刊：179-181．
41．孟军，魏同立，吴金，常昌远．数字图像离散小波变换的原理与硬件实现分析．东南大学学报(自然科学版)，2002，32(6)：1-6．
42．韦志辉，程军．基于小波变换的一种新的图像质量评价方法．南京理工大学学报，1998，22(6)：552-555．
43．甘斌，张雄伟，甘仲民．基于小波变换的多尺度图像边缘处理．数字电视与数字视频，2001．8：20-22．
44．黄应清，季向琦，张智诠．图像小波系数的分布规律及其对图像质量的影响．红外与激光工程，2000，29(3)：15-18．
45．张新明，沈兰荪．基于小波和统计特性的自适应图像增强．信号处理，2001，17(3)：227-231．
46．鞠彦忠，阎贵平，陈建斌，陈景彦，陈建华．用小波神经网络检测结构损伤．工程力学，2003，20(6)：176-181．
47．张雄伟，曹铁勇编著．DSP芯片的原理与开发应用(第二版)．北京：电子工业出版社．2001．
48．申敏，邓矣兵等编著．DSP原理及其在移动通信中的应用．北京：人民邮电出版社．2001．
49．Uwe．Meyer-Baese著，刘凌，胡永生译．数字信号处理的FPGA实现．北京：清华大学出版社，2003．
50．《中国集成电路大全》编委会编著．专用集成电路和集成系统自动化设计方法．北京：国防工业出版社，1997．
51．潘松，王国栋编著．VHDL实用教程．成都：电子科技大学出版社，2000．
52．袁祥辉．固体图像传感器及其应用．重庆：重庆大学出版社，1996．
53．盛骤，谢式千，潘承毅编．概率论与数理统计(第二版)．北京：高等教育出版社，2000．
54. TMS320VC5402 Fixed-Point Digital Signal Processor. TI, 2000.
55. TMS320C54x DSP Reference Set Volume 1: CPU and Peripherals. TI, 2001.
56. TMS320C54x DSP Reference Set Volume 2: Mnemonic instruction Set. TI, 2001.
57. TMS320C54x DSP Reference Set Volume 3: Algebraic Instruction Set. TI, 2001.
58. TMS320C54x DSP Reference Set Volume 4: Applications Guide.TI, 1996.
59. TMS320C54x DSP Reference Set Volume 5: Enhance Peripherals. TI, 1999.
60. TMS320VC5402 and TMS320UC5402 Bootloader. TI, 2002.
61. Spartan-Ⅱ 2.5V FPGA Family: Introduction and Ordering Information. Xilinx, Inc., 2001.
62. Spartan-Ⅱ 2.5V FPGA Family: Functional Description. Xilinx, Inc., 2001.
63. Spartan-Ⅱ 2.5V FPGA Family: DC and Switching Characteristics. Xilinx, Inc., 2002.
64. Spartan-Ⅱ 2.5V FPGA Family: Pinout Tables. Xilinx, Inc., 2001.
65. Configuring FPGAs Over a Processor Bus. Xilinx, Inc., 1995.
66. ISO/IEC JTCI/SC29 WG1, JPEG 2000 Part Ⅰ Final Committee Draft Version 1.0. 2000.
67. Cheol-Hee Park, Jong-Ho Paik, Young-Hwan You, Hyoung-Kyu Song, Yong-Soo Cho, Auto focus filter design and implementation using correlation between filter and auto focus criterion. International Conference on ICCE 2000, 2000:250-251.
68. Dickson, S. D., Tyminski, J. K., McNamara, S. J., Humphrey, D. C., Ziemer, D., FC2: focus control technique for critical level i-line photolithography. Advanced Semiconductor Manufacturing Conference and Workshop, 1997,10(12): 309-316.
69. Moreira, A., Mittermayer, J., Scheiber, R., A SAR auto-focus technique based on azimuth scaling. Geoscience and Remote Sensing, 1997, 4: 2028-2030.
70. Komatsu, F., Motaki, H., Miyoshi, M., A new auto-focus method in critical dimension measurement SEM. Test Symposium, 1997, 17(19): 202-207.
71. Soatto, S., Observability of shape from focus. Decision and Control, 1998, 3:3251-3256.
72. Yoshida, Y., Shinohara, S., Ikeda, H., Tada, K., Yoshida, H., Nishide, K., Sumi, M., Control of lens position in auto-focus cameras using semiconductor laser range finder. Circuits and Systems, 1991, 1: 395-398.
73. E. Hughlett and P. Kaiser, An autofocus technique for imaging microscopy. ICASSP-92, 1992, 3: 93-96.
74. Wen-Hsin Chan, Ching-Twu Youe, Video CCD based portable digital still camera. IEEE Transactions on Consumer Electronics, 1995, 41 (3): 455-459.
75. Koizumi, T., Hwan-Sul Chun, Zen, H., A new optical detector for a high-speed AF control. IEEE Transactions on Consumer Electronics, 1996, 42(4): 1055-1061.
76. Ooi, K., Izumi, K., Nozaki, M., Takeda, I., An Advanced Auto-Focus System for Video Camera Using Quasi Condition Reasoning. IEEE 1990 International Conference on Consumer Electronics, 1990: 348-349.
77. Zhang, X., Fukuda, N., Obuchi, Y., Kambe, T., Kubo, N., Kawamura, H., Suzuki, I., A signal processing system on chip for digital cameras. 26th Annual Conference of the IEEE, 2000, 2: 1243-1248.
78. Kwangho Yoon, Chanki Kim, Bumha Lee, Doyoung Lee, Single-chip CMOS image sensor for mobile applications. Solid-State Circuits Conference, 2002, 1: 36-442.
79. Getzlaff, S., Schreiter, J., Graupner, A., Schuffny, R., A system-on-chip realization of a CMOS image sensor with programmable analog image preprocessing. The 2001 IEEE International Symposium on Circuits and Systems, 2001,4: 486-489.
80. Tabet, M., Hornsey, R., CMOS image sensor camera with focal plane edge detection. Canadian Conference on Electrical and Computer Engineering, 2001, 2:1129-1133.
81. Nair, R., Raj, K., Signal processing in CMOS image sensors. 2000 IEEE Workshop on Signal Processing Systems, 2000:801-810.
82. David Atkinson, Derek L. G. Hill, Peter N. R. Stoyle, Paul E. Summers, and Stephen F. Keevil, Automatic Correction of Motion Artifacts in Magnetic Resonance Images Using an Entropy Focus Criterion. IEEE Transactions on Medical Imaging, 1997,16(6): 903-910.
83. Li Xi, Liu Guosui, Jinlin Ni. Autofocusing of ISAR Images Based on Entropy Minimization.
IEEE Transactions on Aerospace and Electronic System, 1999,35(4): 1240-1252.
84. A.D. Brink. Using spatial information as an aid to maximum entropy image threshold selection. Pattern Recognition Letters, 1996,17: 29-36.
85. N.B. Nill and B. H. Bouzas, Objective Image Quality Measures Derived From Digital Image Power Spectra, Optical Engineering, 1992, 31 (4): 813-825.
86. K. Falconer, Fractal Geometry - Mathematical Foundations and Applications. Wiley, New York (1990).
87. Y. Itakura, S. Tsutsumi, and T. Takagi, Statistical properties of the background noise for the atmospheric windows in the intermediate infrared region. Infrared Physics, 1974, 14:17-29.
88. B. R. Hunt and T. M. Cannon, Nonstationary assumptions for gaussian models of images. IEEE Trans. Syst., 1976, 6 (12): 876-882.
89. D.J. Granrath, The role of human visual models in image processing. Proc. IEEE, 1981, 69 (5): 552-561.
90. N. B. Nill, A visual model weighted cosine transform for image compression and quality assessment. IEEE Trans. Commun. 1985, 33 (6): 551-557.
91. J.J. DePalma and E. M. Lowry, Sine wave response of the visual system. Ⅱ. Sine wave and square wave contrast sensitivity. J. Opt. Soc. Am, 1962, 52 (3): 328-335.
92. J.L. Mannos and D. J. Sakrison, The effects of a visual fidelity criterion on the encoding of images. IEEE Trans. Inform. Theory, 1974, 20 (4): 525-536.
93. K. N. Ngan, K. S. Leong, and H. Singh, Adaptive cosine transform coding of images in perceptual Domain. IEEE Trans. Acoust. Speech Signal Process. 1989, 37 (11): 1743-1750.
94. E.A. Fadhel and P. Bhattacharyya, Application of a Steerable Wavelet Transform using Neural Network for Signature Verification. Pattern Analysis & Applications, 1999, 2: 184-195.
95. B.J.W. Fleming, D. Yu, R.G. Harrison, and D. Jubb. Wavelet-based detection of coherent structures and self-affnity in financial data. Eur. Phys. J. B, 2001,(20): 543-546.
96. Z. Peng, Y. He, Q. Lu and F. Chu. Feature Extraction of the Rub-impact Rotor System By Means of Wavelet Analysis. Journal of Sound and Vibration, 2003,259(4): 1000-1010.
97. M. Antonopoulos-Domis, T. Tambouratzis. System Identification a Transient via Wavelet Multiresolution Analysis. Ann. Nucl. Energy, 1998, 25(6): 465-480.
98. I. Urriza, J. I. Artigas, L. A. Barragan, J. I. Garcia and D. Navarro. VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images. Real-Time Imaging, 2001, 7: 203-217.
99. L. Bruzzone, F. Roli and S.B. Serpico. Structure neural networks for signal classification. Signal Processing, 1998,64:271-290.
100. Steven K. Rogers, John M. Colombi, Curtis E. Martin, etc. Neural Networks for Automatic Target Recognition. Neural Networks, 1995,8(7): 1153-1184.
101. Krzysztof J. Cios, Inbo Shin. Image Recognition Neural Network: IRNN. Neurocomputing, 1995,7: 159-185.
102. A. Ravichandran, B. Yegnanarayana. Studies on Object Recognition From Degraded Images Using Neural Networks. Neural Networks, 1995,8(3): 481-488.
103. J. Antonio Catalan, J.S. Jin and T. Gedeon. Reducing the Dimensions of Texture Features for Image Retrieval Using Multilayer Neural Networks. Pattern Analysis & Applications, 1999,2: 196-203.
104. D. de Ridder, R.P.W. Duin, P.W. Verbeek and L.J. van Vliet. The Applicability of Neural Networks to Non-linear Image Processing. Pattern Analysis & Applications, 1999,2: 111-128.
105. A. Datta, S. Pal and S. Chakraborti. Image Thinning by Neural Networks. Neural Comput & Applications, 2002,11: 122-128.
106. S. Singh, A. Amin2. Neural Network Recognition of Hand-printed Characters. Neural Cornput & Applications, 1999,8: 67-76.
107. Mandana Ebadian Dehkordi, Nasser Sherkat, Tony Allen. Handwriting style classification. IJDAR, 2003,6: 55-74.
108. P.A. Fadhel and E Bhattacharyya. Application of a Steerable Wavelet Transform using Neural Network for Signature Verification. Pattern Analysis & Applications, 1999,2: 184-195.