人工免疫算法及其在图像增强中的应用
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摘要
生物免疫系统是一个高度进化的生物系统,它具有高度自适应、高度分布性、自组织等特性。它能够有效识别入侵的抗原并消除抗原,并保持机体的稳定。随着人们对免疫系统研究的进一步深入,免疫系统强大的识别能力引起了许多学者的关注。人工免疫算法是一种受生物免疫系统启发而设计的新型智能优化算法。它结合了问题的先验知识和生物免疫系统的自适应能力,因而具有在信息处理方面有较强的鲁棒性、在求解优化问题时不要求目标函数具有可导性等附加信息、在搜索过程中能更好地收敛到全局最优解等特点,现已被用于机器学习、异常和故障诊断、机器人行为仿真、函数优化、网络入侵检测等多个领域,表现出强大的性能和效率,被人们认为是最具潜力的人工智能算法之一。
本文首先对生物免疫系统的一些基本概念、系统组成、功能及原理进行了介绍;简单分析了人工免疫系统的研究内容、研究现状及基本理论;其次,研究和分析了现有的一些典型免疫算法的基本结构和流程。
其次,在分析了传统图像增强原理和方法的基础上,针对克隆选择算法的不足进行了讨论及多方面的改进,提出了一种改进的克隆选择算法,并实际编程实现了改进的克隆选择算法。然后,使用一种新的目标函数评价算法的性能,将新的适应度函数结合改进的克隆选择算法进行图像增强。使用此方法可以自适应找出图像归一化的非完全Beta函数的最优参数值,对原始图像进行仿真实现,仿真结果证明其在增强后视觉效果有较大提高。
最后,分析了改进的克隆选择算法的搜索速度及参数改变对算法性能的影响,验证了算法的有效性和鲁棒性。并与其他算法的实际结果进行了比较,进一步说明了改进的克隆选择算法的有效性。
Biological immune system is a highly parallel adaptive information learning system, which can identify and remove the antigenic eyeliners invading the body. With people's immune system further in-depth study, the immune system caused by a strong ability to identify a lot of the attention of scholars. Artificial immune algorithm is a kind of new intelligent optimization algorithm which is inspired by biological immune system. Because this algorithm combines the prior knowledge and the adaptive ability of immune system, it has some characteristics as follow : robust in information processing; not requiring derivable additional information of the objective function in solving optimization problem; be able to find better global optimal solution in the process of searching. Now this kind of algorithm has been used in many fields in which showing excellent performance and efficiency, such as machine learning, unconventionality and malfunction diagnosis, simulation of the behavior of robots, control of robots, intrusion detection of networks, function optimization and so on, so it is considered to be one of the most potential intelligent search algorithms.
First of all, in this paper, some basic concepts, framework, functions and principles of the biological immune system are introduced. Then the research range, research status and basic theory of the artificial immune system are simply analyzed.
Secondly, based on the analysis on classical image enhancement methods and principle, considering the deficiency of the clone selection algorithm, some improvements are made and then an improved clone selection algorithm is proposed. This algorithm is also been realized by programming and a project to search the optimal or suboptimal coefficients. A new objective function is used to evaluate the performance of algorithm, and new adaptive function combined with intelligence optimum algorithm to enhance images. Using the approach, the optimization parameters in the normalized incomplete Beta function of degraded images can be automatically find out and can reason the degraded types of the original image correctly. The simulation results prove that the visual effects of degraded images are highly improved after enhancement.
At last, here image enhancement problems are been solved, validating the efficiency of the algorithm. Searching speed of this algorithm and the influence when changing some parameters is discussed, which proved the improved clone selection algorithm is robust. Then the comparison of the results by improved clone selection algorithm to those by other algorithm validated the efficiency of the algorithm once more.
本文首先对生物免疫系统的一些基本概念、系统组成、功能及原理进行了介绍;简单分析了人工免疫系统的研究内容、研究现状及基本理论;其次,研究和分析了现有的一些典型免疫算法的基本结构和流程。
其次,在分析了传统图像增强原理和方法的基础上,针对克隆选择算法的不足进行了讨论及多方面的改进,提出了一种改进的克隆选择算法,并实际编程实现了改进的克隆选择算法。然后,使用一种新的目标函数评价算法的性能,将新的适应度函数结合改进的克隆选择算法进行图像增强。使用此方法可以自适应找出图像归一化的非完全Beta函数的最优参数值,对原始图像进行仿真实现,仿真结果证明其在增强后视觉效果有较大提高。
最后,分析了改进的克隆选择算法的搜索速度及参数改变对算法性能的影响,验证了算法的有效性和鲁棒性。并与其他算法的实际结果进行了比较,进一步说明了改进的克隆选择算法的有效性。
Biological immune system is a highly parallel adaptive information learning system, which can identify and remove the antigenic eyeliners invading the body. With people's immune system further in-depth study, the immune system caused by a strong ability to identify a lot of the attention of scholars. Artificial immune algorithm is a kind of new intelligent optimization algorithm which is inspired by biological immune system. Because this algorithm combines the prior knowledge and the adaptive ability of immune system, it has some characteristics as follow : robust in information processing; not requiring derivable additional information of the objective function in solving optimization problem; be able to find better global optimal solution in the process of searching. Now this kind of algorithm has been used in many fields in which showing excellent performance and efficiency, such as machine learning, unconventionality and malfunction diagnosis, simulation of the behavior of robots, control of robots, intrusion detection of networks, function optimization and so on, so it is considered to be one of the most potential intelligent search algorithms.
First of all, in this paper, some basic concepts, framework, functions and principles of the biological immune system are introduced. Then the research range, research status and basic theory of the artificial immune system are simply analyzed.
Secondly, based on the analysis on classical image enhancement methods and principle, considering the deficiency of the clone selection algorithm, some improvements are made and then an improved clone selection algorithm is proposed. This algorithm is also been realized by programming and a project to search the optimal or suboptimal coefficients. A new objective function is used to evaluate the performance of algorithm, and new adaptive function combined with intelligence optimum algorithm to enhance images. Using the approach, the optimization parameters in the normalized incomplete Beta function of degraded images can be automatically find out and can reason the degraded types of the original image correctly. The simulation results prove that the visual effects of degraded images are highly improved after enhancement.
At last, here image enhancement problems are been solved, validating the efficiency of the algorithm. Searching speed of this algorithm and the influence when changing some parameters is discussed, which proved the improved clone selection algorithm is robust. Then the comparison of the results by improved clone selection algorithm to those by other algorithm validated the efficiency of the algorithm once more.
引文
[1]莫宏伟.人工免疫系统原理与应用[M].哈尔滨工业大学出版社.2003.1-390.
[2] Gonzalez Rafael C.Digital Image Processing Second Edition[M].RUAN Qiu-qi etal Translate. Beijing: Publishing House of Electronics Industry, 2003.59-132.
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[5] Jon Timmis, Mark Neal, John Hunt.An artificial immune system for data analysis [J]. Biosystem, 2000(55):143-150.
[6] Jerne, N.K. Towards a Network Theory of the Immune System[J]. Annual Immunology, 1974, 125(C):373-389.
[7] Tang Z,Yamaguchi T,etc. A multiple valued immune network and its applications [J].1999, E82-A(6):1102-1108.
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[43]靳示信,刘光远等.一种改进的用于多峰值函数优化的自适应克隆选择算法[J].西南大学学报(自然科学版), 2007,29(3):164-168.
[44] LU Chen-ying ,DELGADO-FRIAS J G,LIN W.A clustering and genetic scheme for large TSP optimization problem [J].Cybernetics and Systems,1998,29(2):137-157.
[45] Tarakanov, A. O., Skormin, V. A..Pattern recognition by immune computing [A]. Evolutionary Computation, 2002. CEC’02 Proceedings of the 2002 Congress[C]. 2006:938-943.
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[47]张纪会,高齐圣,徐心和.自适应蚁群算法[J].控制理论与应用,2000,17(1):1-8.
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[2] Gonzalez Rafael C.Digital Image Processing Second Edition[M].RUAN Qiu-qi etal Translate. Beijing: Publishing House of Electronics Industry, 2003.59-132.
[3] Leandro Nunes de Castro,Fernando JoséVon Zubenmm. Artificial Immune Systems: PartI–Basic Theory and Applications[D].1999(1):76-85.
[4] Leandro Nubes de Castro, Fernadro JoséVon Zubenmm. Artificial Immune Systems: PartII-A Survey of Applications [D].2001(1):28-59.
[5] Jon Timmis, Mark Neal, John Hunt.An artificial immune system for data analysis [J]. Biosystem, 2000(55):143-150.
[6] Jerne, N.K. Towards a Network Theory of the Immune System[J]. Annual Immunology, 1974, 125(C):373-389.
[7] Tang Z,Yamaguchi T,etc. A multiple valued immune network and its applications [J].1999, E82-A(6):1102-1108.
[8]王磊,潘近,焦李成.免疫算法[J].电子学报,2000,28(7):74-78.
[9] Dasgupa D, Forrest S. Artificial immune systems in industrial applications. In:Proceedings of the Second International Conference on Intelligent Proceeding and Manufacturing of Materials, 1999(1):257-267.
[10] Lee JD.Digital image enhancement and noise filter by use of local statistic[J].IEEE Trans PAMI: 1997, 19(9):863-872.
[11] YongKui K, DooKwon B. Vehicle classification algorithm for single loop detectors using neural networks. IEEE Transactions on Vehicular Technoloy, 2006,55(6):1704-1711.
[12] Chun JS,Kim MK,Jung HK, etc.Shape optimization of electrum agnatic devices using immune algorithm. IEEE Trans on Magetics, 1997, 33(2):1876-1879.
[13] Farmer J D,Packard NH, Perelson AS. The Immune System, Adaptation, and Machine Learning [J].Physical D, 1986, 22:187-204.
[14] Neal M, Hunt J., Timmis,J. Augmenting an artificial immune network [A].Systems, Man, and Cybemetics,1998 IEEE international Conference[C].1998:3821-3826.
[15] De Castro L N, Von zuben F J. Artificial Immune Systems: Part-I Basic Theory and Application[R].Technical Report-RT DCA,1999,(01):89.
[16] Chun J S ,Jung H K, Hahn S Y.A study on comparison of optimization of performance between immune algorithm and other heuristic algorithms Magnetic[J].1998,34(5):2972-2975.
[17] Du Haifeng, Jiao Licheng, Liu Ruochen. Adaptive Polyclonal Programming Algorithm with Applications ICCMA 03[C].2003
[18]王磊,潘近,焦李成.免疫规划[J].计算机学报,2000,23(8):806-812.
[19] Lee J D.Digital image enhancement and noise filter by use of local statistics [J].IEEE Trans PAMI, 1997, 19(9):863-872..
[20] Ramar K, Arumugam S, Sivanandam S N. Enhancement of noisy and blurred images: a fuzzy operator approach [J].Advances in Modeling and Analysis,1999,42(1):49-60.
[21]谢克明,谢刚,郭红波等.人工免疫系统及其算法[J].电子与信息学报,2005,27(11).
[22]杨黎明.一种改进的免疫遗传算法及在PID控制器优化设计中的应用[D].长沙:中南大学,2006.
[23] Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm: Adaptation in Immune Systems as an Evolution[C].ICEC96, 1996:150-153.
[24] Cheng H V, Xu H. A novel fuzzy logic approach to contrast enhancement [J]. Pattern Recognition, 2000, 33(5):809-819.
[25] Tubbs JD. A note on parametric image enhancement [J].Pattern Recognition, 1987, 30(6):617-621.
[26] Cserey G, Roska T. An artificial immune systems for visual application with CNN-UM(A).Circuits and Systems,2004.ISCAS04.Proceedings of the 2004 International Symposium[C] 2004:17-20.
[27] P.J. Costa Bronco. Using Immunology Principles for Fault Detection [J].IEEE Transactions on Industrial Electionics, 2007, 50(2):362-373.
[28] You Yong, Wang Sun’an, Sheng,Wangxing. Short-term load forecasting using artificial immune network[A]. Power System Technology, 2002 Proceedings Power on 2002 International Conference[C]. 2002:2322-2325.
[29] Meshref H, Vanlandingham H. Immune network simulation of reactive control of a robot arm manipulator [A]. Soft Computing in Industrial Applications, 2001.Proceedings of the 2001 IEEE Mountain Workshop on[C].2001:81-85.
[30] Timmis J, Neal M, Hunt J. Data analysis using artificial immune systems, cluster analysis and networks: some comparisons [A]. Systems, Man, and Cybernetics, 1999. IEEESMC’99 Conference Proceedings [C]. 1999 IEEE International Conference, 1999:922-927.
[31]周激流,吕航.一种基于新型遗传算法的图像自适应增强算法的研究[J].计算机学报,2001,24(9):959-964.
[32]刘丽钰,蔡自兴.一种改进的克隆选择优化算法[J].计算机工程与应用,2006,5(13):30-32.
[33]蔡自兴,龚涛.免疫算法研究的进展[J].控制与决策,2004,19(8):841-846.
[34]席裕庚,恽为民.遗传算法综述[J].控制理论与应用,1996,13(6):697-708.
[35]陈书海,傅录祥.实用数字图像处理[M].北京:科学出版社,2005.
[36]朱秀昌,刘峰.数字图像处理与图像通信[M].北京邮电大学出社,2002,68-72.
[37]龚非力.医学免疫学[M].北京:科学出版社.2003.1.1-27.
[38] Ge Hong, Mao Zong-Yuan. Immune Algorithm, Proceedings of the 4th World Congress on Intelligent Control and Automation.2002, 6.1784-1787.
[39]肖人彬,王磊.人工免疫系统:原理、模型、分析及展望[J].计算机学报.2002,25(12), 1281-1293.
[40]王飞跃,词计算和语言动力学系统基本问题和研究[J],自动化学报,2005, 31(6),844-852.
[41] Timmis J,Neal,M . A resource limited artificial immune system for data analysis [J]. Knowledge-Based Systems, 2001, 14:121-130.
[42]丁永生,任立红.人工免疫系统:理论与应用[J].模式识别与人智,2000,13(1):52-59.
[43]靳示信,刘光远等.一种改进的用于多峰值函数优化的自适应克隆选择算法[J].西南大学学报(自然科学版), 2007,29(3):164-168.
[44] LU Chen-ying ,DELGADO-FRIAS J G,LIN W.A clustering and genetic scheme for large TSP optimization problem [J].Cybernetics and Systems,1998,29(2):137-157.
[45] Tarakanov, A. O., Skormin, V. A..Pattern recognition by immune computing [A]. Evolutionary Computation, 2002. CEC’02 Proceedings of the 2002 Congress[C]. 2006:938-943.
[46] Forrest S, Javornik B, Smith R E. Using Genetic Algorithms to Exp lore Pattern Recognition in the Immune System [J]. Evolutionary Computation,2005,1(3): 191-211.
[47]张纪会,高齐圣,徐心和.自适应蚁群算法[J].控制理论与应用,2000,17(1):1-8.
[48] F.Russo, G.Ramponi. An Image Enhancement Technique based on the FIRE Operator. In IEEE International Conference on Image Processing(ICIP-95).Washington DC,USA.OCT,11-25,1995.1:155-158.
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