臺灣博碩士論文加值系統

在這篇論文中，我們提出了利用粒子群優化來解決雜訊影像和預測問題。
首先，提出了一種新的排序型開關中值濾波器（SSMF）可以來針對極端損壞的影像做去除雜訊，同時亦可保留影像細節的部分。在檢測階段，中心像素點將會被辨識出是“未損壞”或“損壞”的像素點。排序型開關中值濾波器在處理損壞的像素點時，周圍擁有較多的未損壞像素點者，將在過濾階段擁有更高的優先濾波處理。在五種影像雜訊模型及其實驗下，所提出的SSMF的性能，能有效的去除高雜訊密度（雜訊密度範圍從10％到90％）的灰階和彩色影像，實驗證實我們所提出的SSMF大大優於其他現有的中值濾波器。
其次，我們提出了自適應影像使用非區域方法並配合動盪的粒子群優化（TPSO）基於一個沒有參考影像的度量Q。在大多數實際情況，即使在沒有提供“無雜訊”的參考影像下，濾波器亦可處理雜訊影像。在本文中，我們結合TPSO和NLM以提出TPNLM的過濾器植基於度量Q。所提出的過濾器能夠在不需要任何相關之參考影像下去除高斯雜訊，同時保持精細的影像細節，邊緣和紋理。實驗在幾個加了未知情況的高斯雜訊影像模擬下，證明所提出的方法在去除雜訊的性能表現，無論是在視覺、峰值訊噪比（PSNR）和結構相似性指數（SSIM）等數據下都比現有的方法還要好。
第三，提出了修改動盪粒子群優化（MTPSO）方法用於溫度預測和台灣期貨交易指數（TAIFEX）預測，植基於兩個因素的模糊時間序列和粒子群優化。MTPSO模型可以更容易且準確地處理模糊時間序列的時間間隔長度和內容預測規則等兩個主要影響因素。溫度預測及期貨指數預測實驗結果顯示，所提出之模型是優於任何現有的模型，它可以在高階模糊時間序列下得到更優質的解決方案。
最後，新的基金趨勢及交易策略模型結合動盪粒子群優化（TPSO）和均線技術指標用來尋找適當的技術指標參數，以實現高利潤低風險的共同基金操作。所提出的方法利用移動平均線的時間間隔和交易模型來提供了幾個很好的基金交易買入和賣出點以減少損失。實驗結果說明，該模型的性能比原有的基金效能更好。

In this dissertation, noise image filters and Prediction problems based on particle swarm optimization are proposed.
Firstly, a novel sorted switching median filter (i.e. SSMF) for effectively denoising extremely corrupted images while preserving the image details is proposed. The center pixel is considered as “uncorrupted” or “corrupted” noise in the detecting stage. The corrupted pixels that possess more noise-free surroundings will have higher processing priority in the SSMF sorting and filtering stages to rescue the heavily noisy neighbors. Five noise models are considered to assess the performance of the proposed SSMF algorithm. Several extensive simulation results conducted on both grayscale and color images with a wide range (from 10% to 90%) of noise corruption clearly show that the proposed SSMF substantially outperforms all other existing median-based filters.
Secondly, an adaptive image denoising algorithm that uses the non-local means in conjunction with the turbulent particle swarm optimization (i.e. TPSO) which based on a no-reference metric Q is presented. The proposed noise image filter can deal with the noisy image even if no “noise-free” reference is available in most practically circumstances. We combined TPSO and NLM to propose the TPNLM filter. The proposed filter is able to denoising Gaussian noise without need for any knowledge about the noise-free image, at the same time preserving fine image details, edges and textures well. We also demonstrate several simulations with images contaminated by additive Gaussian noise under unknown noise variance to show that the performance of the proposed method surpasses those of previously published works, both in visual and in terms of peak signal to noise ratio (PSNR) and the structural similarity index (SSIM), respectively.
Thirdly, a modified turbulent particle swarm optimization (named MTPSO) method is proposed for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experiment results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively.
Finally, a new funds trading strategy that combines turbulent particle swarm optimization (named TPSO) and mixed moving average techniques is presented and used to find the proper content of technical indicators’ parameters to achieve high profit and low risk on the mutual funds. The time interval of moving average of the proposed method is adjustable and the trading model could avoid and reduce loss by providing several good buying and selling points. The experiment results show that the performance of the proposed model is better than the best original performance.

論文摘要 I
Abstract III
List of Contents VI
List of Tables IX
List of Figures XII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivations 4
1.3 Organization of the dissertation 5
Chapter 2 Particle swarm optimization 6
2.1 Standard particle swarm optimization 7
2.2 Turbulent particle swarm optimization 8
Chapter 3 Sorted switching median filter for impulse noise 11
3.1 Preliminaries 11
3.2 Noise models 12
3.2.1 Noise model 1 13
3.2.2 Noise model 2 13
3.2.3 Noise model 3 14
3.2.4 Noise model 4 14
3.2.5 Noise model 5 14
3.3 The sorted switching median filter 15
3.3.1 Detecting stage 15
3.3.2 Sorting stage 19
3.3.3 Filtering stage 20
3.4 Experiment results and discussions for SSMF 21
3.4.1.1 Based on noise model 1 24
3.4.1.2 Based on noise model 2 29
3.4.1.3 Based on noise model 3 30
3.4.1.4 Based on noise model 4 31
3.4.1.5 Based on noise model 5 32
3.4.1.6 Color images 34
Chapter 4 Turbulent particle swarm optimization image non-local means filter for Gaussian noise 37
4.1 Preliminaries 37
4.2 Non-local means 40
4.3 No-reference image metric Q 42
4.4 The TPNLM filter 44
4.5 Experiment results and discussions for TPNLM 45
4.5.1 Experiment result for Convergence of the TPNLM 47
4.5.2 Experiment result for the values of MSE and metric Q 48
4.5.3 Experiment results for different images 50
4.5.4 Experiment results for different denoising filters 58
4.5.5 Experiment results for computational complexity 61
Chapter 5 Modify turbulent particle swarm optimization fuzzy forecasting method 62
5.1 Preliminaries 62
5.2 The definition of new fuzzy time series forecasting procedure 63
5.3 The MTPSO model 80
5.4 Illustration of MTPSO model 83
5.5 Experiment results of the MTPSO model 87
5.5.1 Experiment results for the training phase 87
5.5.2 Experiment results for the testing phase 92
Chapter 6 Mixed moving averages and particle swarm optimization fund forecasting trend and trading strategy model 96
6.1 Preliminaries 96
6.2 Moving average in trading strategies 97
6.3 The MMAPSO model 100
6.3.1 Trading strategy 100
6.3.2 Mixed moving averages and TPSO algorithm 106
6.4 Illustration of MMAPSO model 106
6.5 Experiment results for MMAPSO 109
6.5.1 Experiment results for the stability of MMAPSO 111
6.5.2 Experiment results for the ROIs in recent 10 years. 111
Chapter 7 Conclusions and future works 115
Bibliography 117

[1](2000). cnYES.com. Available: http://www.cnyes.com
[2]A. Buades, B. Coll, and Morel, J., "On image denoising methods," Technical Report 2004-15, CMLA, 2004.
[3]Abadpour, A. and Kasaei, S., "An efficient PCA-based color transfer method," Journal of Visual Communication and Image Representation, vol. 18, pp. 15-34, 2007.
[4]Abrahim, B. A., Mustafa, Z. A., and Kadah, Y. M., "Modified non-local means filter for effective speckle reduction in ultrasound images," in National Radio Science Conference (NRSC), pp. 1-8, 2011.
[5]Atiya, A., Talaat, N., and Shaheen, S., "An efficient stock market forecasting model using neural networks," in International Joint Conference on Neural Networks (IJCNN), pp. 2112-2115 vol.4, 1997.
[6]Awate, S. P. and Whitaker, R. T., "Unsupervised, information-theoretic, adaptive image filtering for image restoration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 364-376, 2006.
[7]Azzabou, N., Paragios, N., and Guichard, F., "Image denoising based on adapted dictionary computation," in IEEE International Conference on Image Processing (ICIP), pp. III - 109-III - 112, 2007.
[8]Baba, N. and Kozaki, M., "An intelligent forecasting system of stock price using neural networks," in International Joint Conference on Neural Networks (IJCNN), pp. 371-377 vol.1, 1992.
[9]Brox, T., Kleinschmidt, O., and Cremers, D., "Efficient nonlocal means for denoising of textural patterns," IEEE Transactions on Image Processing, vol. 17, pp. 1083-1092, 2008.
[10]Buades, A., Coll, B., and Morel, J. M., "A non-local algorithm for image denoising," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 60-65 vol. 2, 2005.
[11]Chen, S.-M., "Forecasting enrollments based on fuzzy time series," Fuzzy Sets and Systems, vol. 81, pp. 311-319, 1996.
[12]Chen, S.-M. and Hwang, J.-R., "Temperature prediction using fuzzy time series," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 30, pp. 263-275, 2000.
[13]Chen, T., Ma, K.-K., and Chen, L.-H., "Tri-state median filter for image denoising," IEEE Transactions on Image Processing, vol. 8, pp. 1834-1838, 1999.
[14]Chen, W.-M., Lai, C.-J., Wang, H.-C., Chao, H.-C., and Lo, C.-H., "H.264 video watermarking with secret image sharing," IET Image Processing, vol. 5, pp. 349 – 354, 2011.
[15]Chen, W.-M., Lo, C.-H., Chao, H.-C., and Chang, C.-C., "Gabor filter aided 3D ultrasonography diagnosis system with WLAN transmission consideration," Journal of Universal Computer Science, vol. 16, pp. 1327-1342, 2010.
[16]Cheng, C.-H., Chen, T.-L., Teoh, H.-J., and Chiang, C.-H., "Fuzzy time-series based on adaptive expectation model for TAIEX forecasting," Expert Systems with Applications, vol. 34, pp. 1126-1132, 2008.
[17]Cheng, S.-C. and Hsia, S.-C., "Fast algorithms for color image processing by principal component analysis," Journal of Visual Communication and Image Representation, vol. 14, pp. 184-203, 2003.
[18]Chou, H.-H., Hsu, L.-Y., and Hu, H.-T., "Turbulent-PSO-based fuzzy image filter with no-reference measures for high-density impulse noise," IEEE Transactions on Cybernetics, vol. 43, pp. 296-307, 2013.
[19]Chu, H.-H., Chen, T.-L., Cheng, C.-H., and Huang, C.-C., "Fuzzy dual-factor time-series for stock index forecasting," Expert Systems with Applications, vol. 36, pp. 165-171, 2009.
[20]Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., and Barillot, C., "An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images," IEEE Transactions on Medical Imaging, vol. 27, pp. 425-441, 2008.
[21]Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K., "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Transactions on Image Processing, vol. 16, pp. 2080-2095, 2007.
[22]De Causmaecker, P., Demeester, P., and Vanden Berghe, G., "A decomposed metaheuristic approach for a real-world university timetabling problem," European Journal of Operational Research, vol. 195, pp. 307-318, 2009.
[23]Dorigo, M., "Optimization, learning and natural algorithms " PhD thesis, Politecnico di Milano, Italy, 1992.
[24]Dourra, H. and Siy, P., "Investment using technical analysis and fuzzy logic," Fuzzy Sets and Systems, vol. 127, pp. 221-240, 2002.
[25]Duval, V., Aujol, J.-F., and Gousseau, Y., "A bias-variance approach for the nonlocal means," SIAM Journal on Imaging Sciences, vol. 4, pp. 760-788, 2011.
[26]Eberhart, R. and Shi, Y., "Comparison between genetic algorithms and particle swarm optimization," in Evolutionary Programming VII. vol. 1447, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds., ed: Springer Berlin Heidelberg, 1998, pp. 611-616.
[27]Eberhart, R. C. and Shi, Y., "Comparing inertia weights and constriction factors in particle swarm optimization," in Proceedings of the Congress on Evolutionary Computation, pp. 84-88 vol.1, 2000.
[28]Elbeltagi, E., Hegazy, T., and Grierson, D., "Comparison among five evolutionary-based optimization algorithms," Advanced Engineering Informatics, vol. 19, pp. 43-53, 2005.
[29]Elder, D. A., Trading for a living, 1st ed.: John Wiley & Sons, Inc., 1993.
[30]Ellis, C. A. and Parbery, S. A., "Is smarter better? A comparison of adaptive, and simple moving average trading strategies," Research in International Business and Finance, vol. 19, pp. 399-411, 2005.
[31]Eng, H.-L. and Ma, K.-K., "Noise adaptive soft-switching median filter," IEEE Transactions on Image Processing, vol. 10, pp. 242-251, 2001.
[32]Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., and PremChand, C. H., "Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter," IEEE Signal Processing Letters, vol. 18, pp. 287-290, 2011.
[33]Felsberg, M., "Autocorrelation-driven diffusion filtering," IEEE Transactions on Image Processing, vol. 20, pp. 1797-1806, 2011.
[34]Fogel, L. O., A.J.; Walsh, M.J., Artificial intelligence through simulated evolution, 1st ed., 1966.
[35]Gao, D., Wu, X., Shi, G., and Zhang, L., "Color demosaicking with an image formation model and adaptive PCA," Journal of Visual Communication and Image Representation, vol. 23, pp. 1019-1030, 2012.
[36]Gilboa, G. and Osher, S., "Nonlocal linear image regularization and supervised segmentation," Technical Report CAM-06-47, Department of Mathematics, University of California at Los Angeles, CA, U.S.A.,2006.
[37]Gouchol, P., Jyh-Charn, L., and Nair, A. S., "Selective removal of impulse noise based on homogeneity level information," IEEE Transactions on Image Processing, vol. 12, pp. 85-92, 2003.
[38]Grewenig, S., Zimmer, S., and Weickert, J., "Rotationally invariant similarity measures for nonlocal image denoising," Journal of Visual Communication and Image Representation, vol. 22, pp. 117-130, 2011.
[39]Grudnitski, G. and Osburn, L., "Forecasting S&P and gold futures prices: An application of neural networks," Journal of Futures Markets, vol. 13, pp. 631-643, 1993.
[40]Gunasekarage, A. and Power, D. M., "The profitability of moving average trading rules in South Asian stock markets," Emerging Markets Review, vol. 2, pp. 17-33, 2001.
[41]Ho, J. and Hwang, W.-L., "Wavelet bayesian network image denoising," IEEE Transactions on Image Processing, vol. 22, pp. 1277-1290, 2013.
[42]Holland, J. H., Adaptation in natural and artificial systems, 1st ed.: University of Michigan Press, 1975.
[43]Horng, S.-J., Hsu, L.-Y., Li, T., Qiao, S., Gong, X., Chou, H.-H., et al., "Using sorted switching median filter to remove high-density impulse noises," Journal of Visual Communication and Image Representation, 2013.
[44]Hsiao, P.-C. and Chang, L.-W., "Image denoising with dominant sets by a coalitional game approach," IEEE Transactions on Image Processing, vol. 22, pp. 724-738, 2013.
[45]Hsu, L.-Y., Horng, S.-J., Fan, P., Chou, H.-H., Wang, X., and Guo, M., "Adaptive non-local means for image denoising using turbulent PSO with no-reference measures," International Symposium on Biometrics and Security Technologies, 2013.
[46]Hsu, L.-Y., Horng, S.-J., Fan, P., Khan, M. K., Wang, Y.-R., Run, R.-S., et al., "MTPSO algorithm for solving planar graph coloring problem," Expert Systems with Applications, vol. 38, pp. 5525-5531, 2011.
[47]Hsu, L.-Y., Horng, S.-J., He, M., Fan, P., Kao, T.-W., Khan, M. K., et al., "Mutual funds trading strategy based on particle swarm optimization," Expert Systems with Applications, vol. 38, pp. 7582-7602, 2011.
[48]Hsu, L.-Y., Horng, S.-J., Kao, T.-W., Chen, Y.-H., Run, R.-S., Chen, R.-J., et al., "Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques," Expert Systems with Applications, vol. 37, pp. 2756-2770, 2010.
[49]Hsu, Y.-T., Hung, H.-F., Yeh, J., and Liu, M.-C., "Profit refiner of futures trading using clustering algorithm," Expert Systems with Applications, vol. 36, pp. 6192-6198, 2009.
[50]Huang, Y.-L., Horng, S.-J., He, M., Fan, P., Kao, T.-W., Khan, M. K., et al., "A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization," Expert Systems with Applications, vol. 38, pp. 8014-8023, 2011.
[51]Huarng, K., "Effective lengths of intervals to improve forecasting in fuzzy time series," Fuzzy Sets and Systems, vol. 123, pp. 387-394, 2001(a).
[52]Huarng, K., "Heuristic models of fuzzy time series for forecasting," Fuzzy Sets and Systems, vol. 123, pp. 369-386, 2001(b).
[53]Huarng, K. and Yu, H. K., "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, vol. 353, pp. 445-462, 2005.
[54]Hwang, H. and Haddad, R. A., "Adaptive median filters: new algorithms and results," IEEE Transactions on Image Processing, vol. 4, pp. 499-502, 1995.
[55]Jayaraj, V. and Ebenezer, D., "A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in images," EURASIP J. Adv. Signal Process, vol. 2010, pp. 1-11, 2010.
[56]Kaye, M., The standard & poor's guide to selecting stocks, 1st ed.: McGraw-Hill, Inc., 2006.
[57]Kennedy, J. and Eberhart, R., "Particle swarm optimization," in International Joint Conference on Neural Networks (IJCNN), pp. 1942-1948 vol.4, 1995.
[58]Kennedy, J., Eberhart, R. C., and Shi, Y., "The particle swarm," in Swarm Intelligence, 1st ed San Francisco: Morgan Kaufmann, 2001.
[59]Kuo, I.-H., Horng, S.-J., Chen, Y.-H., Run, R.-S., Kao, T.-W., Chen, R.-J., et al., "Forecasting TAIFEX based on fuzzy time series and particle swarm optimization," Expert Systems with Applications, vol. 37, pp. 1494-1502, 2010.
[60]Lam, S. S., "A genetic fuzzy expert system for stock market timing," in Proceedings of the Congress on Evolutionary Computation, pp. 410-417 vol. 1, 2001.
[61]Lee, L.-W., Li-Hui, W., Chen, S.-M., and Leu, Y.-H., "Handling forecasting problems based on two-factors high-order fuzzy time series," IEEE Transactions on Fuzzy Systems, vol. 14, pp. 468-477, 2006.
[62]Lee, L.-W., Wang, L.-H., and Chen, S.-M., "Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms," Expert Systems with Applications, vol. 33, pp. 539-550, 2007.
[63]Lee, L.-W., Wang, L.-H., and Chen, S.-M., "Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques," Expert Systems with Applications, vol. 34, pp. 328-336, 2008.
[64]Li, Z., Chen, Z., and Li, J., "A model of weather forecast by fuzzy grade statistics," Fuzzy Sets and Systems, vol. 26, pp. 275-281, 1988.
[65]Lien, C.-Y., Huang, C.-C., Chen, P.-Y., and Lin, Y.-F., "An efficient denoising architecture for removal of impulse noise in images," IEEE Transactions on Computers, vol. 62, pp. 631-643, 2013.
[66]Lin, S.-Y., Horng, S.-J., Kao, T.-W., Fahn, C.-S., Huang, D.-K., Run, R.-S., et al., "Solving the bi-objective personnel assignment problem using particle swarm optimization," Applied Soft Computing, vol. 12, pp. 2840-2845, 2012.
[67]Lindenbaum, M., Fischer, M., and Bruckstein, A., "On Gabor's Contribution to Image Enhancement," Pattern Recognition, vol. 27, pp. 1-8, 1994.
[68]Liu, H. and Abraham, A., "Fuzzy adaptive turbulent particle swarm optimization," in International Conference on Hybrid Intelligent Systems (HIS), p. 6 pp., 2005.
[69]M. Tkalcic and J. F. Tasic, "Color spaces: Perceptual, historical and applicational background," in Proc. IEEE EUROCON, pp. 304-308, 2003.
[70]Ng, P.-E. and Ma, K.-K., "A switching median filter with boundary discriminative noise detection for extremely corrupted images," IEEE Transactions on Image Processing, vol. 15, pp. 1506-1516, 2006.
[71]Perona, P. and Malik, J., "Scale-space and edge detection using anisotropic diffusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 629-639, 1990.
[72]Pring, M. J., Technical analysis explained, 3rd ed.: McGraw-Hill Inc., 1991.
[73]Salmon, J., "On two parameters for denoising with non-Local means," IEEE Signal Processing Letters, vol. 17, pp. 269-272, 2010.
[74]Salmon, J. and Strozecki, Y., "From patches to pixels in non-local methods: weighted-average reprojection," in IEEE International Conference on Image Processing (ICIP), pp. 1929-1932, 2010.
[75]Salmon, J. and Strozecki, Y., "Patch reprojections for non-local methods," Signal Processing, vol. 92, pp. 477-489, 2012.
[76]Shi, Y. and Eberhart, R. C., "A modified particle swarm optimizer," in IEEE International Conference on Evolutionary Computation Proceedings, pp. 69-73, 1998.
[77]Shi, Y. and Eberhart, R. C., "Fuzzy adaptive particle swarm optimization," in Proceedings of the Congress on Evolutionary Computation, pp. 101-106 vol. 1, 2001.
[78]Shuqun, Z. and Karim, M. A., "A new impulse detector for switching median filters," IEEE Signal Processing Letters, vol. 9, pp. 360-363, 2002.
[79]Singh, S. R., "A simple method of forecasting based on fuzzy time series," Applied Mathematics and Computation, vol. 186, pp. 330-339, 2007(a).
[80]Singh, S. R., "A robust method of forecasting based on fuzzy time series," Applied Mathematics and Computation, vol. 188, pp. 472-484, 2007(b).
[81]Singh, S. R., "A computational method of forecasting based on fuzzy time series," Mathematics and Computers in Simulation, vol. 79, pp. 539-554, 2008.
[82]Song, Q. and Chissom, B. S., "Fuzzy time series and its models," Fuzzy Sets and Systems, vol. 54, pp. 269-277, 1993(a).
[83]Song, Q. and Chissom, B. S., "Forecasting enrollments with fuzzy time series -- Part I," Fuzzy Sets and Systems, vol. 54, pp. 1-9, 1993(b).
[84]Song, Q. and Chissom, B. S., "Forecasting enrollments with fuzzy time series -- part II," Fuzzy Sets and Systems, vol. 62, pp. 1-8, 1994.
[85]Srinivasan, K. S. and Ebenezer, D., "A new fast and efficient decision-based algorithm for removal of high-density impulse noises," IEEE Signal Processing Letters, vol. 14, pp. 189-192, 2007.
[86]Sun, T. and Neuvo, Y., "Detail-preserving median based filters in image processing," Pattern Recognition Letters, vol. 15, pp. 341-347, 1994.
[87]Tang, Y., Xu, F., Wan, X., and Zhang, Y.-Q., "Web-based fuzzy neural networks for stock prediction," in Computational intelligence and applications, ed: Dynamic Publishers, Inc., 2002, pp. 169-174.
[88]Thirilogasundari, V., babu, V. S., and Janet, S. A., "Fuzzy based salt and pepper noise removal using adaptive switching median filter," Procedia Engineering, vol. 38, pp. 2858-2865, 2012.
[89]Toh, K. K. V. and Isa, N. A. M., "Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction," IEEE Signal Processing Letters, vol. 17, pp. 281-284, 2010.
[90]Tukey, J. W., Exploratory data analysis, 1st ed.: Reading, MA: Addison-Wesley, 1971.
[91]Wang, Z. and Zhang, D., "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, pp. 78-80, 1999.
[92]Wu, T.-Y., Chen, C.-Y., Kuo, L.-S., Lee , W.-T., and Chao, H.-C., "Cloud-based image processing system with priority-based data distribution mechanism," Computer Communications, vol. 35, pp. 1809–1818, 2012.
[93]Yaroslavsky., L., "Digital picture processing - an introduction," Springer Verlag, 1985.
[94]Yu, T. H. K. and Huarng, K. H., "A bivariate fuzzy time series model to forecast the TAIEX," Expert Systems with Applications, vol. 34, pp. 2945-2952, 2008.
[95]Zadeh, L. A., "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965.
[96]Zhou, W., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, pp. 600-612, 2004.
[97]Zhu, X. and Milanfar, P., "Automatic parameter selection for denoising algorithms using a no-reference measure of image content," Image Processing, IEEE Transactions on, vol. 19, pp. 3116-3132, 2010.
[98]Zhu, X. and Milanfar, P., "A no-reference image content metric and its application to denoising," in IEEE International Conference on Image Processing (ICIP), pp. 1145-1148, 2010.