數位影像的品質容易被環境因素所影響，例如相機的抖動、雜訊的干擾、或是傳送過程中資料的遺失等等。因此，影像濾波器常被當成數位影像處理系統中的一個前處理模組，用來解決這類的問題。然而，大多數的影像濾波器都是著重在排序計算影像所包含的資訊，或是分析位置和權重的關係，無法處理高雜訊比的影像。若影像遭到嚴重的毀損時，一般影像濾波器往往無法處理，就被迫要放棄這張影像。本論文中所採用的模糊推論濾波器架構有別於一般傳統濾波器的架構，利用預先建構好的知識庫裡面的資訊來推論產生濾波器輸出，有別於大多數影像濾波器直接使用雜訊影像的資訊做計算，具有可以處理高雜訊比影像的優勢；並且採用基因演算法來調整濾波器架構參數，使濾波器能夠達到最佳的效能。最後本論文更進一步的分析影像的特性，提出一個初始族群設定法來設定基因演算法的初始族群，縮小參數搜尋的範圍，並增進參數學習的效率。

The quality of digital images is affected under many circumstances, such as camera shaking, noise disturbance, and data lose during transmission. To solve these problems, image filters have long been a pre-processing module in the system of digital image processing. However, most digital filters put great emphasis on sorting the information contained in the images or analyzing the relationship between positions and weights, and do not have the ability to process the images with high noise ratio. In this thesis, we adopt a fuzzy inference filter configuration that utilizes a pre-constructed database to infer the output of the filters. This configuration is different from most filters which directly use noisy images for computation, and therefore has the advantages of dealing with high noise images. Moreover, we adopt a genetic algorithm to tune the parameters of the filter to obtain an optimal filtering performance. Finally, we analyze the characteristics of the images and propose a population initialization method to improve the quality of initial populations. This initialization method not only minimizes the range of parameter searching but improves the efficiency of parameter learning.

[1] D. Wan and J. Zhou, “Fingerprint recognition using model-based density map,”
IEEE Trans. on Image Processing, vol. 15, no. 6, pp. 1690-1696, June 2006.
[2] F. Goudail, E. Lange, T. Iwamoto, K. Kyuma, and N. Ostu, “Face recognition systems using local autocorrelations and multiscale integration,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1024-1028, October 1996.
[3] S. Tsugawa, “Vision-based vehicles in Japan: machine vision systems and driving control systems,” IEEE Trans. on Industrial Electronics. vol. 41, no. 4, pp. 398-405, August 1994.
[4] A. K. Jain, Fundamentals of Digital Image Processing, Prentice –Hall, New York,
1992.
[5] S. J. Ko and Y. H. Lee, “Center weighted median filters and their applications to
image enhancement,” IEEE Trans. on Circuit and Systems for Video Technology, vol. 38, no. 9, pp. 984-993, September 1991.
[6] X. Yang and P. S. Toh, “Adaptive fuzzy multilevel median filter,” IEEE Trans. on
Image Processing, vol. 4, no. 5, pp. 680-682, May 1995.
[7] C. S. LEE, Y. H. Kuo, and P. T. Yu, “Weighted fuzzy mean filters for image
processing,” Fuzzy Sets and Systems, vol. 89, pp.187-180, 1997.
[8] E. Abreu and S. K. Mitra, “A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images”, in Proc. IEEE International Conference on Acoustics, Speech, Signal Process., Detroit, MI, pp. 2371-2374, 1997.
[9] F. Russo, “Hybrid neuro-fuzzy filter for impulse noise removal,” Pattern
Recognition, vol. 32, pp. 4843-1855, 1999.
[10] F. Russo, “Noise removal from image data using recursive neurofuzzy filters,”
IEEE Trans. on Instrumentation and Measurement, vol. 49, no. 2, pp. 307-314, April 2000.
[11] J.H. Wang, W.J. Liu and L.D. Lin, “Histogram-based fuzzy filter for image
restoration,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 32, no. 2, pp. 230-238, April 2002.
[12] R. Lukac, “Adaptive vector median filtering,” Pattern Recognition Letters, vol. 24, pp. 1889-1899, 2003.
[13] T. C. Lin and P. T. Yu “Partition fuzzy median filter based on fuzzy rules for image restoration,” Fuzzy Sets and Systems, vol. 147, pp. 75-97, 2004.
[14] K. Arakawa, “Median filter based on fuzzy rules and its application to image restoration,” Fuzzy Sets and Systems, vol. 77, pp. 3-13, 1996
[15] J.Y. Chang and J.L. Chen, “Classified-augmented median filters for image restoration,” IEEE Trans. on Instrumentation and Measurement, vol. 53, no.2, pp.351-356, April 2004.
[16] P. Liu, “Representation of digital image by fuzzy neural network,” Fuzzy Sets and Systems, vol. 130, pp.109-123, 2002.
[17] C.S. Lee, S.M. Kuo, And C. Y. Hsu “Genetic-based fuzzy image filter and its application to image processing,” IEEE Trans. on System, Man, and Cybernetics, Part B, vol. 35, no. 4, pp. 624-711, August 2005.
[18] F. Russo, “A method for estimation and filtering of Gaussian noise in images,” IEEE Trans. on Instrumentation and Measurement, vol. 4 pp. 1148-1154, August 2003.
[19] F. Russo, “Automatic enhancement of noisy images using objective evaluation of image quality,” IEEE Trans. on Instrumentation and Measurement, vol. 54, pp. 1600-1606, August 2005.
[20] F. Russo “An image enhancement technique combining sharpening and noise reduction,” IEEE Trans. on Instrumentation and Measurement, vol. 4, pp. 824-828, August 2002.
[21] H. Ghezelayagh and K. Y. Lee,“Intelligent predictive control of a power plant with evolutionary programming optimizer and neuro-fuzzy identifier,” Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1308-1313, May 2002.
[22] S. Zahan and C. Michael, “A fuzzy hierarchical approach to medical diagnosis,”
Proceedings of the Sixth IEEE International Conference on Fuzzy Systems, vol. 1, pp. 319-324, July 1997.
[23] S. K. Sinha and F. Karray, “Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm,” IEEE Trans. on Neural Networks, vol. 13, no. 2, pp. 393-401, March 2002.
[24] A. Joshi and E. N. Houstis, “On neurobiological, neuro-fuzzy, machine learning,
and statistical pattern recognition techniques,” IEEE Trans. on Neural Networks,
vol. 8, no.1, pp. 18-31, January 1997.
[25] P. Melin, O. Castillo and F. Sotelo, “Automated quality control in sound speaker
manufacturing using a hybrid neuro-fuzzy approach,” IFSA World Congress and 20th NAFIPS International Conference, vol. 2, pp. 1027-1032, July 2001.
[26] A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, pp. 338-351, 1995.
[27] 蘇木春、張孝德，機器學習：類神經網路、模糊系統以及基因演算法則，全華科技股份有限公司，民91。
[28] 周鵬程，遺傳演算法原理與應用—活用Matlab，修訂版，全華科技股份有限公司，民94。
[29] 陳同孝、張真誠、黃國峰，數位影像處理技術Digital Image Processing，旗標出版股份有限公司，民93。
[30] 謬紹剛，數位影像處理Digital Image Processing 2/e，普林斯頓國際有限公司，民93
[31] 連國珍，數位影像處理，儒林圖書有限公司，民92。
[32] 鐘國亮，數位影像處理與電腦視覺，台灣東華書局股份有限公司，民91。
[33] I. Gath and A. B. Geva “Unsupervised optimal fuzzy clustering”, IEEE Trans.
on Pattern Analysis and Machine Intelligence, vol. 11, no.7, pp. 773-780, July. 1989.