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Student Number 974203048 Author Tsunghan Wu(吳宗翰) Author's Email Address No Public. Statistics This thesis had been viewed 513 times. Download 361 times. Department Information Management Year 2009 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Title A Computational Intelligence Based Approach with Complex Fuzzy Sets to Adaptive Image Noise Processing Date of Defense 2010-06-23 Page Count 108 Keyword adaptive image noise cancelling (AINC) clustering complex fuzzy set complex neuro-fuzzy system image restoration PSO RLSE self-organization Abstract In this thesis, we propose two novel adaptive filters, complex neuro-fuzzy system (CNFS) and self-organizing complex neuro-fuzzy system (SOCNFS), and apply them to the problem of adaptive image noise cancelling (AINC). Complex fuzzy sets (CFS) and Takagi-Sugeno (T-S) fuzzy If-Then rules are used to shape the structure of both the CNFS and SOCNFS. A CFS is the fuzzy set whose membership is complex-valued state within the unit disk in complex plane. We devise a hybrid optimization method to adapt the adaptive filters for the AINC problem. The hybrid learning method is called the PSO-RLSE method, including the well-known particle swarm optimization (PSO) method and the famous recursive least square estimation (RLSE) method. They cooperate in hybrid way during the learning process for the adaptive filters. The PSO is used to update the parameters of premise part of the filters while the consequent part is updated by the RLSE. The PSO-RLSE learning method is very efficient for fast learning. The proposed CNFS and SOCNFS filters possess excellent nonlinear mapping ability because CFS can bring in complex memberships in fuzzy inference computing for input-output mapping capability. On the other hand, with the mechanism of self-organization, the SOCNFS can generate fuzzy rules in the form of clusters and learn its parameters by the stimulation of input/output training data to have its initial If–Then rules for application. In the AINC application, the proposed CNFS and SOCNFS can perform indirect function approximation to mimic the dynamic behaviour of unknown noise channel in such a way that a corrupted image may be adaptively restored as clear to its original version as possible. Few examples with several images are used to test the proposed approachs, by which excellent performance for image restoration has been observed. Table of Content 中文摘要i

英文摘要ii

誌謝iii

目錄iv

圖目錄vi

表目錄x

第一章、 緒論1

1.1研究背景1

1.2研究方法與動機1

1.3研究目的4

1.4論文架構5

第二章、 文獻探討6

2.1數位影像資料格式與取樣量化6

2.2常用雜訊消除濾波器9

2.3適應性雜訊消除問題12

2.4影像品質評估機制15

第三章、適應性濾波器設計方法17

3.1複數類神經模糊適應性濾波器17

3.1.1 複數模糊集合17

3.1.2 T-S模糊推理系統架構19

3.2 自我組織複數類神經模糊適應性濾波器23

3.2.1 以分群為基礎的自我組織機制23

3.2.2 模糊C均值分裂演算法26

3.3 複合式學習演算法29

3.3.1 粒子群最佳化演算法29

3.3.2 遞迴最小平方估計法30

3.3.3 PSO-RLSE複合式學習演算法32

第四章、 適應性濾波器應用於影像雜訊消除34

第五章、 CNFS與SOCNFS進行影像雜訊消除38

5.1應用CNFS適應性濾波器解決AINC問題38

5.2應用SOCNFS適應性濾波器解決AINC問題61

5.3實驗數據分析與討論88

第六章、結論與未來工作92

參考文獻93

簡歷96Reference [1]D. Van De Ville, M. Nachtegael, D. Van der Weken, E. E. Kerre, W. Philips, and I. Lemahieu, “Noise reduction by fuzzy image filtering,” IEEE Transactions on Fuzzy Systems, vol. 11, pp. 429-436, 2003.

[2]G. A. Mastin, “Adaptive filters for digital image noise smoothing-An evaluation,” Computer Vision, and Image Processing, vol. 31, pp. 103-121, 1985.

[3]B. Smolka, R. Lukac, A. Chydzinski, K. N. Plataniotis, and W. Wojciechowski, “Fast adaptive similarity based impulsive noise reduction filter,” Real-Time Imaging, vol. 9, pp. 261-276, 2003.

[4]R. C. Gonzalez and RE Woods. Digital Image Processing, Addison-Wesley Publishing Company, New York, 1993.

[5]C. F. Juang and C. T. Lin, “An online self-constructing neural fuzzy inference network and its applications,” IEEE Transactions on Fuzzy Systems, vol. 6, pp. 12-32, 1998.

[6]S. Paul and S. Kumar, “Subset hood-product fuzzy neural inference system (SuPFuNIS), ” IEEE Transactions on Neural Networks, vol. 13, pp. 578-599, 2002.

[7]V. N. Vapnik, S. Golowich, and A. J. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” Advances in Neural Information Processing Systems, vol. 9, pp. 281-287, 1997.

[8]V. V. Sergeev, V. N. Kopenkov, and A. V. Chernov, “Comparative Analysis of Function Approximation Methods in Image Processing ,” Pattern Recognition and image analysis, vol. 17, pp. 217-221, 2007.

[9]W. A. Farag, V. H. Quintana, and G. Lambert-Torres, "A genetic-based neuro-fuzzy approach for modelling and control of dynamical systems," IEEE Transactions on Neural Networks, vol. 9, pp. 756-767, 1998.

[10]A. Kandel, D. Ramot, R. Milo, and M. Friedman, "Complex Fuzzy Sets," IEEE Transactions on Fuzzy Systems, vol. 10, pp. 171–186, 2002.

[11]S. Dick, "Toward complex fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 13, pp. 405-414, 2005.

[12]D. Ramot, M. Friedman, G. Langholz, and A. Kandel, "Complex fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 11, pp. 450-461, 2003.

[13]R. C. Eberhart and J. Kennedy, “A New Optimizer Using Particle Swarm Theory,” Proceeding of the Sixth International Symposium on Micro Machine and Human Science, vol. 43, pp. 39-43, 1995.

[14]P. J. Angeline, “Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences,” Lecture Notes in Computer Science, pp. 601-610, 1998.

[15]M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE transactions on Evolutionary Computation, vol. 6, pp. 58-73, 2002.

[16]X. Hu and R. Eberhart, “Multiobjective optimization using dynamic neighborhood particle swarm optimization,” Proceeding of the Evolutionary Computation, pp. 1677-1681, 2002.

[17]Y. Dong, J. Tang, B. Xu, and D. Wang, “An application of swarm optimization to nonlinear programming,” Computers and Mathematics with Applications, vol. 48, pp. 1655-1668, 2005.

[18]G. C. Goodwin and R. L. Payne, Dynamic system identification: Experiment design and data analysis: Academic Press, 1997.

[19]S. Pena, R. S. Alonso, Anigstein, and B. A. Cone, “Robust optimal solution to the attitude/force control problem,” IEEE Transactions on Aerospace and Electronic Systems, vol. 36, pp. 784-792, 2000.

[20]I. Bilik and J. Tabrikian, “Radar target classification using doppler signatures of human locomotion models,” IEEE Transactions on Aerospace and Electronic Systems, vol. 43, pp. 1510-1522, 2007.

[21]J. J. Hopfield and D. W. Tank, “Computing with neural circuits: A model,” Science, vol. 233, pp. 625-633, 1986.

[22]S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, Inc., New York, 1994.

[23]B. Kosko, “Fuzzy systems as universal approximators,” IEEE Transactions. Comput., vol. 43, pp. 1329-1333, 1994.

[24]J. A. Dickerson and B. Kosko, “Fuzzy function approximation with ellipsoidal rules,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 26, pp. 542-560, 1996.

[25]Y. Yang, J. J. Shi, J. E. Harry, J. Proctor, C. P. Garner, and M. G. Kong, "Multilayer plasma patterns in atmospheric pressure glow discharges," IEEE Transactions on Plasma Science, vol. 33, pp. 302-303, 2005.

[26]T. Nakano and T. Suda, “Self-organizing network services with evolutionary adaptation,” IEEE Transactions on Neural Networks, vol. 16, pp. 1269-1278, 2005.

[27]G. Polzlbauer, T. Lidy, and A. Rauber, “Decision manifolds—a supervised learning algorithm based on self-organization,” IEEE Transactions on Neural Networks, vol. 19, pp. 1518-1530, 2008.

[28]A. H. Tan, N. Lu, and D. Xiao, "Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback," IEEE Transactions on Neural Networks, vol. 19, pp. 230-244, 2008.

[29]F. Russo, “Noise removal from image data using recursive neurofuzzy filters,” IEEE Transactions on Instrumentation and Measurement, vol. 49, pp. 307-314, 2000.

[30]Widrow, J. R. Glover, and J.M. McCool, “Adaptive noise cancelling: Principles and application”, Proc. IEEE, vol. 63, pp. 1692-1730, 1975.

[31]Chunshien Li, Kuo-Hsiang. Cheng, “Soft Computing Approach to Adaptive Noise Filtering”, IEEE Proceedings Conference on Cybernetics and Intelligent Systems, Singapore, 2004.

[32]B. Widrow and S. D. Stearns, “Adaptive signal processing,” Prentice Signal Processing Series, pp.474, 1985.

[33]Gonzalez, R. C. “Richard. E. Woods,“Digital Image Processing.” 2nd International Edition Prentice Hall, 2002.

[34]A. C. Zelinski, M. Puschel, S. Misra, and J. C. Hoe “Automatic cost minimization for multiplierless implementations of discrete signal transforms,” IEEE ICASSP, vol. 5, pp. 221-225, 2004.

[35]J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing, Prentice Hall Upper Saddle River, NJ, 1997.

[36]H. Sun, S. Wang, and Q. Jiang, “FCM-based model selection algorithms for determining the number of clusters,” Pattern recognition, vol. 37, pp. 2027-2037, 2004.

[37]K. J. Astrom and B. Wittenmark, Adaptive control: Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1994.Advisor Chunshien Li(李俊賢)

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974203048.pdf Date of Submission 2010-07-26

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