基於模糊邏輯之影像前處理技術研究

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康仲嘉(Chung-Chia Kang) 查詢紙本館藏

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電機工程學系

論文名稱

基於模糊邏輯之影像前處理技術研究
(The Study on Some Image Pre-Processing Techniques Based on Fuzzy Logic)

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摘要(中)

在電腦視覺以及圖形識別等影像處理技術中，前處理技術扮演很重要的角色，其中包括脈衝雜訊濾除（impulse noise removal）、邊緣偵測（edge detection）以及影像分割（image segmentation）等技術。這些技術的運算結果對於後續影像處理程序的效能有非常大的影響。然而，影像資訊包含千百種不同的內容，想要設計一個理想的前處理技術來適用於所有的影像內容非常困難。值得一提的是，模糊邏輯（fuzzy logic）對於具不確定性的資訊具有優異的處理能力且廣泛地被應用於工程領域。因此，在本論文中，我們嘗試設計數個基於模糊邏輯之新影像前處理技術。針對脈衝雜訊，我們提出一個新的模糊濾波器，並應用粒子群最佳化（particle swarm optimization, PSO）來訓練調整濾波器參數，使其具有最佳的雜訊濾除效果。針對邊緣偵測，我們應用模糊邏輯設計一個最大化目標函數（maximizing objective function）計算每一個邊緣方向的邊緣強度，在影像中偵測出更正確且更細緻的邊緣資訊。最後，針對影像分割，我們簡化所提出的邊緣偵測法並延伸應用於改良種子區塊成長（seeded region growing, SRG）影像分割法，使得影像可以被更準確且更適宜地被分割。

摘要(英)

Image pre-processing techniques play a key role in image processing applications such as computer vision and pattern recognition. The common image pre-processing techniques contain impulse noise removal, edge detection, image segmentation, and etc. Good performances of those techniques lead to the better results of the consequent image processing techniques. However, the variety of the image contents is too numerous to count. It is difficult to design an exact image pre-processing technique that can cover all possible image contents and can obtain good results in all images. Fuzzy logic is very popular in engineering applications with its capability to deal with the typical uncertainty that characterizes any physical system. Therefore, this dissertation tries to design some fuzzy logic based image pre-processing techniques such that the performances of the conventional pre-processing are improved. In impulse noise removal, the fuzzy logic based impulse noise filter is proposed. The particle swarm optimization learning process is used to optimize the parameters of the filter such that the best noise removal capability is obtained. In edge detection, the fuzzy logic is used to define a maximizing objective function, in which the edge intensities of each edge direction are determined. Hence, the more correct edge without the double edge and thick edge are detected. In image segmentation, fuzzy logic and the simplified proposed edge detection method are used to improve the conventional seeded region growing for image segmentation. Consequently, the more correct and the more appropriate segmentation results than other existing methods are obtained.

關鍵字(中)

★ 影像處理
★ 脈衝雜訊濾除
★ 邊緣偵測
★ 影像分割
★ 模糊邏輯

關鍵字(英)

★ image processing
★ impulse noise removal
★ edge detection
★ image segmentation
★ fuzzy logic

論文目次

Abstract i
Contents ii
List of Figures v
List of Tables viii
Nomenclature ix
Chapter 1 Introduction
1.1 Background and motivation 1
1.2 Reviews of previous works 2
1.3 Organization and objective 7
Chapter 2 Fuzzy Logic Based Impulse Noise Removal
2.1 Introduction 9
2.2 Modified switching median (MSWM) filter 11
2.2.1 The main algorithm of MSWM 12
2.2.2 Parameters selection 13
2.3 Fuzzy region discrimination based impulse noise filter (FRDF) 18
2.3.1 Four partitions 18
2.3.2 The fuzzy classifier 19
2.3.3 Local statistics and the resultant filter 26
2.4 Using PSO learning process to train the FRDF 28
2.4.1 Reviews of PSO learning process 29
2.4.2 Basic definition of PSO 32
2.4.3 Initialization of the particles' positions 34
2.4.4 Cost function, training image, and termination condition 34
2.5 Simulation results 36
2.6 Summary 44
Chapter 3 Fuzzy Logic Based Edge Detection
3.1 Introduction 45
3.2 Fuzzy logic based edge detection method 46
3.2.1 Basic definition 46
3.2.2 The objective function of edge detection method 47
3.2.3 Using fuzzy logic in the distance measurements 48
3.2.4 Non-maxima suppression 49
3.2.5 The process steps of the fuzzy logic based edge detection method 51
3.3 Simulation results 51
3.4 Summary 59
Chapter 4 Fuzzy Logic Based Seeded Region Growing for Image Segmentation
4.1 Introduction 60
4.2 Fuzzy logic based seeded region growing 61
4.2.1 YCbCr color space 61
4.2.2 Connected edge detection 62
4.2.3 Seeds selection 63
4.2.4 Regions growing 66
4.2.5 Regions merging 67
4.3 Simulation results 67
4.4 Summary 70
Chapter 5 Conclusions and Future Works
5.1 Conclusions 71
5.2 Future works 72
References 73
Publications List 82

參考文獻

[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, New York, USA, 1992.
[2] L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice-Hall, New Jersey, USA, 1994.
[3] T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Letters, vol. 15, pp. 341-347, 1994.
[4] T. Chen, K. K. Ma, and L. H. Chen, “Tri-state median for image denoising,” IEEE Transactions on Image Processing, vol. 8, no. 12, pp. 1834-1838, 1999.
[5] T. Chen and H. R. Wu, “Adaptive impulse detection using center-weighted median filters,” IEEE Signal Processing Letters, vol. 8, no. 1, pp. 1-3, 2001.
[6] T. Chen and H. R. Wu, “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 48, no. 8, pp. 784-789, 2001.
[7] S. Zhang and M. A. Karim, “A new impulse detector for switching median filters,” IEEE Signal Processing Letters, vol. 9, no. 11, pp. 360-363, 2002.
[8] I. Aizenberg and C. Butakoff, “Effective impulse detector based on rank-order criteria,” IEEE Signal Processing Letters, vol. 11, no. 3, pp. 363-366, 2004.
[9] Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, vol. 46, no. 1, pp. 78-80, 1999.
[10] W. Luo, “A new efficient impulse detection algorithm for the removal of impulse noise,” IEICE Transactions on Fundamentals of Electronics, Communications, and Computer Sciences, vol. E88-A, no. 10, pp. 2579-2586, 2005.
[11] W. Luo, “A new impulse detector based on order statistics,” AE? International Journal of Electronics and Communications, vol. 60, no. 6, pp. 462-466, 2006.
[12] W. Luo, “Efficient removal of impulse noise from digital images,” IEEE Transactions on Consumer Electronics, vol. 52, no. 2, pp. 523-527, 2006.
[13] G. Pok, J.C. Liu, and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information,” IEEE Transactions on Image Processing, vol. 12, no. 1, pp. 85-92, 2003.
[14] J. Y. Chang and J. L. Chen, “Classifier-augmented median filters for image restoration,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 2, pp. 351-356, 2004.
[15] L. Jin, C. Xiong, and D. Li, “Selective adaptive weighted median filter,” Optical Engineering, vol. 47, no. 3, pp. 1-5, 2008.
[16] Y. Dong and S. Xu, “A new directional weighted median filter for removal of random-valued impulse noise,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 193-196, 2007.
[17] T. C. Lin and P. T. Yu, “Salt-pepper impulse noise detection and removal using multiple thresholds for image restoration,” Journal of Information Science and Engineering, vol. 22, pp. 189-198, 2006.
[18] J. H. Wang and L. D. Lin, “Improved median filter using minmax algorithm for image processing,” Electronics Letters, vol. 33, no. 16, pp. 1362-1363, 1997.
[19] V. Crnojevic, V. Senk, and Z. Trpovski, “Advanced impulse diction based on pixel-wise MAD,” IEEE Signal Processing Letters, vol. 11, no. 7, pp. 589-592, 2004.
[20] K. S. Srinivasan and D. Ebenezer, “A new fast and efficient decision-based algorithm for removal of high-density impulse noises,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 189-192, 2007.
[21] N. Alajlan, M. Kamel, and E. Jernigan, “Detail preserving impulsive noise removal,” Signal Processing: Image Communication, vol. 19, pp. 993-1003, 2004.
[22] R. Garnett, T. Huegerich, C. Chui, and W. He, “A universal noise removal algorithm with an impulse detector,” IEEE Transactions on Image Processing, vol. 14, no. 11, pp. 1747-1754, 2005.
[23] Y. Dong, R. H. Chan, and S. Xu, “A detection statistic for random-valued impulse noise,” IEEE Transaction on Image Processing, vol. 16, no. 4, pp. 1112-1120, 2007.
[24] Z. F. Deng, Z. P. Yin, and Y. L. Xiong, “High probability impulse noise-removing algorithm based on mathematical morphology,” IEEE Signal Processing Letters, vol. 14, no. 1, pp. 31-34, 2007.
[25] D. Zhang and Z. Wang, “Impulse noise removal using polynomial approximation,” Optical Engineering, vol. 37, no. 4, pp. 1275-1282, 1998.
[26] H. L. Eng and K. K. Ma, “Noise adaptive soft-switching median filter,” IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 242-251, 2001.
[27] P. E. Ng and K. K. Ma, “A switching median filter with boundary discriminative noise detection for extremely corrupted images,” IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1506-1516, 2006.
[28] D. Zhang and Z. Wang, “Impulse noise detection and removal using fuzzy techniques,” Electronics Letters, vol. 33, no. 5, pp. 378-379, 1997.
[29] S. Schulte, M. Nachtegael, V. De Witte, D. Van der Weken, and E. E. Kerre, “A fuzzy impulse noise detection and reduction method,” IEEE Transactions on Image Processing, vol. 15, no. 5, pp. 1153-1162, 2006.
[30] S. Schulte, V. De Witte, M. Nachtegael, D. Van der Weken, and E. E. Kerre, “Fuzzy two-step filter for impulse noise reduction from color images,” IEEE Transactions on Image Processing, vol. 15, no.11, pp. 3567-3578, 2006.
[31] S. Schulte, V. De Witte, M. Nachtegael, D. Van der Weken, and E. E. Kerre, “Fuzzy random impulse noise reduction method,” Fuzzy Set and Systems, vol. 158, no. 3, pp. 270-283, 2007.
[32] E. Beşdok, P. ?ivicioğlu, and M. Al?ı, “Using an adaptive neuro-fuzzy inference system-based interpolant for impulsive noise suppression from highly distorted images,” Fuzzy Sets and Systems, vol. 150, no. 3, pp. 525-543, 2005.
[33] W. Luo, “An efficient detail-preserving approach for removing impulse noise in images,” IEEE Signal Processing Letters, vol. 13, no. 7, pp. 413-416, 2006.
[34] J. H. Wang, W. J. Liu, and L. D. Lin, “Histogram-based fuzzy filter for image restoration,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 32, no. 2, pp. 230-238, 2002.
[35] T. C. Lin and P. T. Yu, “Partition fuzzy median filter based on fuzzy rules for image restoration,” Fuzzy Sets and Systems, vol. 147, no. 1, pp. 75-97, 2004.
[36] T. C. Chiang, C. H. Chiu, and W. J. Wang, “A fuzzy rule based adaptive center weighted median filter,” International Journal of Computer Applications in Technology, vol. 27, no. 2/3, pp. 192-203, 2006.
[37] F. Farbiz, M. B. Menhaj, S. A. Motamedi, and M. T. Hagan, “A new fuzzy logic filter for image enhancement,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 30, no. 1, pp. 110-119, 2000.
[38] 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, no. 4, pp. 429-436, 2003.
[39] F. Russo, “Recent advances in fuzzy techniques for image enhancement,” IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 6, pp. 1428-1434, 1998.
[40] F. Russo, G. Ramponi, “A fuzzy filter for images corrupted by impulse noise,” IEEE Signal Processing Letters, vol. 3, no. 6, pp.168-170, 1996.
[41] F. Russo, “Fuzzy systems in instrumentation: fuzzy signal processing,” IEEE Transactions on Instrumentation and Measurement, vol. 45, no.2, pp. 683-689, 1996.
[42] A. Toprak and İ. G?ler, “Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter,” Digital Signal Processing, vol. 17, no. 4, pp. 711-723, 2007.
[43] C. T. Lin and C. S. G.. Lee, Neural Fuzzy Systems, Prentice-Hall, New Jersey, USA, 1996.
[44] J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, New Jersey, USA, 1997.
[45] T. G. Yen, C. C. Kang, and W. J. Wang, “A genetic based fuzzy-neural networks design for system identification,” the proceedings of IEEE International Conference on Systems, Man and Cybernetics. Hawaii, USA, Oct. 10-12, 2005, pp. 672-678.
[46] M. Russo, “FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 3, pp. 373-388, 1998.
[47] O. Cord?n, F. Herrera, and P. Villar, “Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 667-674, 2001.
[48] J. H. Holland, “Genetic algorithms and the optimal allocations of trials,” SIAM Journal of Computing, vol. 2, no. 2, pp. 88-105, 1973.
[49] D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, New York, USA, 1989.
[50] M. E. Y?ksel, “A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise,” IEEE Transactions on Image Processing, vol. 15, no. 4, pp. 928-936, 2006.
[51] M. E. Y?ksel and E. Beşdok, “A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 6, pp. 854-865, 2004.
[52] F. Russo, “Evolutionary neural fuzzy systems for noise cancellation in image data,” IEEE Transactions on Instrumentation and Measurement, vol. 48, no. 5, pp. 915-920, 1999.
[53] F. Russo, “Noise removal from image data using recursive neurofuzzy filters,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 2, pp. 307-314, 2000.
[54] M. E. Y?ksel, A. Başt?rk, and E. Beşdok, “Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network,” EURASIP Journal on Applied Signal Processing, vol. 16, pp. 2451-2461, 2004.
[55] M. E. Y?ksel and A. Başt?rk, “A simple generalized neuro-fuzzy operator for efficient removal of impulse noise from highly corrupted digital images,” AE? International Journal of Electronics and Communications, vol. 59, pp. 1-7, 2005.
[56] M. E. Y?ksel, “A median/ANFIS filter for efficient restoration of digital images corrupted by impulse noise,” AE? International Journal of Electronics and Communications, vol. 60, pp. 628-637, 2006.
[57] P. ?ivicioğlu, “Using uncorrupted neighborhoods of the pixels for impulsive noise suppression with ANFIS,” IEEE Transactions on Image Processing, vol. 16, no. 3, pp.759-773, 2007.
[58] M. E. Y?ksel, and A. Başt?rk, “Efficient removal of impulse noise from highly corrupted digital images by a simple neuro-fuzzy operator,” AE? International Journal of Electronics and Communications, vol. 57, no. 3, pp. 214-219, 2003.
[59] C. S. Lee, S. M. Guo, and C. Y. Hsu, “Genetic-based fuzzy image filter and its application to image processing,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 35, no. 4, pp. 694-711, 2005.
[60] D. Marr and E. Hildreth, “Theory of edge detection,” the proceeding of the Royal Society, London, vol. 207, pp. 197-217, 1980.
[61] J. F. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-698, 1986.
[62] R. R. Rakesh, P. Chaudhuri, and C. A. Murthy, “Thresholding in edge detection: a statistical approach,” IEEE Transactions on Image Processing, vol. 13, no. 7, 2004.
[63] D. S. Kim, W. H. Lee, and I. S. Kweon, “Automatic edge detection using 3×3 ideal binary pixel patterns and fuzzy-based edge thresholding,” Pattern Recognition Letters, vol. 25, pp. 101-106, 2004.
[64] T. Law, H. Itoh, and H. Seki, “Image filtering, edge detection, and edge tracing using fuzzy reasoning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 481-491, 1996.
[65] H. R. Tizhoosh, “Fast fuzzy edge detection,” the proceedings of the North American Fuzzy Information Processing Society, June 27-29, 2002, pp. 239-242.
[66] L. R. Liang and C. G. Looney, “Competitive fuzzy edge detection,” Applied Soft Computing Journal, vol. 3, pp. 123-137, 2003.
[67] R. Nevatia, “A color edge detector and its use in scene segmentation,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 7, no. 11, pp. 820-825, 1977.
[68] G. S. Robinson, “Color edge detection,” Optical Engineering, vol. 16, no. 5, pp.479-484, 1977.
[69] S. D. Zenzo, “A note on the gradient of a multiimage,” Computer Vision, Graphics, and Image Processing, vol. 33, pp. 116-125, 1986.
[70] P. E. Trahanias and A. N. Venetsanopoulos, “Color edge detection using vector order statistics,” IEEE Transactions on Image Processing, vol. 2, no. 2, pp. 259-264, 1993.
[71] P. E. Trahanias and A. N. Venetsanopoulos, “Vector order statistics operators as color edge detectors,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 26, no. 1, pp. 135-143, 1996.
[72] R. Adams and L. Bischof, “Seeded region growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647, 1994.
[73] J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1454-1466, 2001.
[74] J. Fan, G. Zeng, M. Body, and M. S. Hacid, “Seeded region growing: an extensive and comparative study,” Pattern Recognition Letters, vol. 26, no. 8, pp. 1139-1156, 2005.
[75] F. Y. Shih and S. Cheng, “Automatic seeded region growing for color image segmentation,” Image and Vision Computing, vol. 23, no. 10, pp. 877-886, 2005.
[76] Y. Deng and B. S. Manjunath, “Unsupervised segmentation of color-texture regions in images and video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 800-810, 2001.
[77] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” the proceedings of IEEE International Conference on Neural Networks, Perth, Australia, Nov. 27-Dec. 1, 1995, pp. 1942-1948.
[78] R. C. Eberhart and J. Kennedy, “ A new optimizer using particle swarm theory,” the proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Oct. 4-6, 1995, pp. 39-43.
[79] R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” the proceedings of IEEE Congress on Evolutionary Computation, Seoul, Korea, May 27-30, 2001, pp. 81-86.
[80] F. van den Bergh and A.P. Engelbrecht, “A cooperative approach to particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225-239, 2004.

指導教授

王文俊(Wen-June Wang)

審核日期

2009-1-8

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