臺灣博碩士論文加值系統

本論文主要在探討如何使用模糊重力搜尋演算法來降低影像縮放時的不協調。影像內插法(Interpolation)區分兩種類型：單一影像與多重影像。前者多使用在圖片修復、重建與局部性放大檢視；而後者大多使用在視覺上，由於可連續取得即時影像，因此可以達到即時縮放效果。在影像縮放處理上，最難部分即是增加或維持影像的銳利度與平滑度，並降低產生的模糊感。
本文所提出的方法是利用傳統的線性(Linear)內插法做修改，使用模糊重力搜尋演算法(Fuzzy GSA)以求得最佳像素補償比例，即使在高倍率縮放時，仍然可以保有清晰的影像。在實驗模擬上，與傳統內插法做比較後，峰值信噪比(PSNR)較高以及影像表現有較佳效果。

This thesis aims to apply fuzzy gravitation search algorithms to decrease the image zooming inconsistent condition. The image interpolation method distinguishes between the two categories: single frame and multi-frame. The latter is often used in visually, due to the continuous access to live images, so a real-time zooming effect can be achieved. The former is mostly used in repair, reconstruction and local pictures to enlarge the view. In the image scaling process, the hardest part is to increase or maintain the sharpness and smoothness of the image and to reduce the blurring.
The proposed method is to modify the traditional linear interpolation method, and make use of the fuzzy gravitational search algorithm in order to achieve optimal compensation rate of pixel. Even if in high-magnification scaling, we still have a clear image. Simulation results demonstrate that the proposed scheme gives a higher peak-signal-to-noise ratio (PSNR) and shows a better images results in comparison with traditional method.

Abstract (Chinese) I
Abstract (English) II
Acknowledgment III
Contents IV
List of Figures VI
List of Tables IX


Chapter 1. Introduction
1.1 Motivation 1
1.2 Thesis Organization 5
Chapter 2. Preliminaries
2.1 Introduction 6
2.2 Traditional Interpolation Methods 8
2.3 Description of Gravitational Search Algorithm 12
2.4 Summary 19
Chapter 3. The Fuzzy Gravitational Search Algorithm Interpolation Scheme
3.1 Introduction 20
3.2 Bilinear Interpolation Method 21
3.3 Overview of Problem Formulation 23
3.4 Fuzzy rules and Membership Functions 25
3.5 Fuzzy GSA-Based Image Zooming Interpolation Scheme 28
3.6 Summary 32
Chapter 4. Experimental Results and Comparison
4.1 Introduction 33
4.2 Test Parameter Settings and Tools 34
4.3 The Test Image of Experimental Results 35
4.4 Comparison with Other Methods 42
4.5 Testing for Region of Interest 46
4.6 The Consumption of Computation time 47
4.7 Summary 49
Chapter 5. Conclusions and Future Study
5.1 Conclusions 50
5.2 Future Study 52
References 53

[1]A. Gilman, D. G. Bailey, and S. Marsland, “Model-based least squares optimal interpolation, in Proc. IVCNZ '09. 24th International Conf. Image and Vision Computing New Zealand, 2009, pp. 124 – 129, Nov. 2009.
[2]C. C. Lee, “Fuzzy logic in controller systems: fuzzy logic control –Part I/II, IEEE Trans. Systems Man and Cybernetic., vol. 20, no. 2, pp. 404-435, 1990.
[3]H. Liu, Z. Xu, and A. Abraham, “Hybrid fuzzy-genetic algorithm approach for crew grouping, in Proc. 5th International Conf. Intelligent Systems Design and Applications, 2005. ISDA’05., pp. 332–337, Sept. 2005.
[4]T. S. Huang and R. Y. Tsai, “Multi-frame image restoration and registration, Adv. Comput. Vision Image Process., vol. 1, no. 2, pp. 317–339, 1984.
[5]Y. Cha and S. Kim, “Edge-forming methods for color image zooming, IEEE Trans. Image Process., vol. 15, no. 8, pp. 2315–2323, Aug. 2006.
[6]A. Gilman, D. G. Bailey, and S. R. Marsland, “Interpolation models for image super-resolution, in Proc. 4th IEEE International Symp. Electronic Design, Test and Applications, 2008. DELTA'08, pp. 55-60, Jane 2008.
[7]A. Gilman, D. G. Bailey, and S. R. Marsland, “Least-squares optimal interpolation for fast image super-resolution, in Proc. 5th IEEE International Symp. Electronic Design, Test and Application, 2010. DELTA '10., pp. 29 – 34, Jan. 2010.
[8]K. Neeraj, K. Deswal Pankaj, J. Mehta, and A. Sethi, “Neural network based single image super resolution, in Proc. 11th Symp. Neural Network Applications in Electrical Engineering (NEUREL), 2012, pp. 213 – 218, Sept. 2012.
[9]Y. Cha, and S. Kim, “The error-amended sharp edge (ease) scheme for image zooming, IEEE Trans. Image Process, vol. 16, no. 6, pp. 1496–1505, June. 2007.
[10]L. Zhang and X. Wu, “An edge-guided image interpolation algorithm via directional filtering and data fusion, IEEE Trans. Image Process., vol. 15, no. 8, pp. 2226–2238, Aug. 2006.
[11]W. Dong, L. Zhang, G. Shi, and X. Wu, “Nonlocal back-projection for adaptive image enlargement, in Proc. 16th International Conf. Image Processing (ICIP), 2009, pp. 349 – 352, Nov. 2009.
[12]M. Unser, A. Aldroubi, and M. Eden, “Fast B-spline transforms for continuous image representation and interpolation, IEEE Trans. Pattern Anal. Mach. Intelligence., vol. 13, no. 3, pp. 277–285, 1991.
[13]E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A gravitational search algorithm, Information Science, vol. 179, pp. 2232–2248, Jun. 2009.
[14]F. Saeidi Khabisi and E. Rashedi, “Fuzzy Gravitational Search Algorithm, in Proc. 2nd International Conf. Computer and Knowledge Engineering (ICCKE), 2012, pp. 156 – 160, Oct. 2012.
[15]P. Kolendo, B. Jaworski, and R. Śmierzchalski, “Power-law fitness function scaling in the evolutionary method of path planning, in Proc. 16th International Conf. Methods and Models in Automation and Robotics (MMAR), 2011, pp. 413–420, Aug. 2011.
[16]T. Lehmann, C. Gonner, and K. Spitzer, “Survey: Interpolation methods in medical image processing, IEEE Trans. Medical Imaging, vol. 18, no. 11, pp. 1049–1075, Nov. 1999.
[17]X. Li and M. T. Orchard, “New edge-directed interpolation, IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.
[18]H. S. Hou and H. C. Andrews, “Cubic splines for image interpolation and digital filtering, IEEE Trans. Signal Process., vol. 26, no. 6, pp. 508–517, Dec. 1978.
[19]R. Keys, “Cubic convolution interpolation for digital image processing, IEEE Trans. Acoust. Speech Signal Process, vol. 29, no. 6, pp. 1153–1160, Dec. 1981.
[20]A. Lazar and R.G. Reynolds, Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets, Artificial Intelligence Laboratory, Department of Computer Science, Wayne State University, 2003.
[21]H.-C. Cheng and W.-J. Wang, “Locally edge-adapted distance for image interpolation based on genetic fuzzy system, Expert Systems with Applications, vol. 37, no. 1, pp. 288–297, Jan. 2010.
[22]T. Blu, P. Thévenaz, and M. Unser, “Linear interpolation revitalized, IEEE Trans. Image Process., vol. 13, no. 5, pp. 710–719, May 2004.
[23]S. Hiroki and M. Scarfone Antonio, “α-divergence derived as the generalized rate function in a power-law system, in Proc. International Symp. Information Theory and its Applications (ISITA), 2014, pp. 130 – 134, Oct. 2014.
[24]Y. Her-Terng, and S. Cheng-Shion, “Chaos synchronization using fuzzy logic controller, Nonlinear Analysis: Real World Applications, vol. 9, no. 4, pp.1800–1810, Sept. 2008.
[25]J. Jantzen, Foundations of fuzzy control, West Sussex (England), 2007.
[26]A. Sombra, F. Valdez, P. Melin and O. Castillo, “A new gravitational search algorithm using fuzzy logic to parameter adaptation, in Proc. IEEE Cong. Evolutionary Computation (CEC), 2013, pp. 1068 – 1074, June 2013.
[27]S. Chang, Z. Cvetkovic, M. Vetterli, “Locally adaptive wavelet-based image interpolation, IEEE Trans. Image Process., pp. 1471–1485. 2006.
[28]T. S. Huang and R. Y. Tsai, “Multi-frame image restoration and registration, Adv. Comput. Vision Image Process., vol. 1, no. 2, pp. 317–339, 1984.
[29]R. W. Schafer and L. R. Rabiner, “A digital signal processing approach to interpolation, in Proc. IEEE, vol. 61, no. 6, pp. 692-702, June 1973.
[30]I. J. Schoenberg, On interpolation by spline functions and its minimal properties, On Approximation Theory, 1964. pp. 109 – 129, June 2013.
[31]T. N. E. Greville, Spline functions, interpolation, and numerical quadrature, in Mathematical Methods for Digital Computers, A. Ralston and H. S. Wilf, Eds. New York: Wiley, 1967.
[32]P. Thevenaz, T. Blu, and M. Unser, Image interpolation and resampling, Handbook of medical imaging, Orlando, FL, USA: Academic Press, Inc., 2000.
[33]T. Blu, P. Thévenaz, and M. Unser, “Minimum support interpolators with optimum approximation properties, in Proc. IEEE Int. Conf. Image Process., 1998, vol. 3, pp. 242–245, Oct. 1998.
[34]Y. Cha and S. Kim, “Edge-forming methods for image zooming, J. Math. Image. Vis., vol. 25, no. 3, pp. 353–364, Oct. 2006.
[35]R. Gonzalez and R. Woods, Digital Image Processing, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 2002.
[36]T. Lehmann, C. Gönner, and K. Spitzer, “Addendum: B-spline interpolation in medical image processing, IEEE Trans. Medical Imaging, vol. 20, no. 7, pp. 660–665, July 2001.
[37]P. Thévenaz, T. Blu and M. Unser, “Interpolation revisited, IEEE Trans. Medical Imaging, vol. 19, no. 7, pp. 739–758, Jul. 2000.
[38]M. Unser, A. Aldroubi, and M. Eden, “Enlargement and reduction of digital images with minimum loss of information, IEEE Trans. Image Process.. vol. 4, no. 3, pp. 247–257, Mar. 1995.
[39]A. Gilman, D. G. Bailey, and S. R. Marsland, “Interpolation models for image super-resolution, in Proc. 4th IEEE International Symp. Electronic Design, Test and Applications, Hong Kong, pp. 55-60, Jan. 2008.
[40]S. E. Reichenbach, and F. Geng, “Two-dimensional cubic convolution, IEEE Trans. Image Process., vol. 12, no. 8, pp. 857-865, Aug, 2003.
[41]J. Z. Shi and S. E. Reichenbach, “Image interpolation by two dimensional parametric cubic convolution, IEEE Trans. Image Process., vol. 15, no. 7, pp. 1857-1870, July 2006.
[42]J.T. Tou and R.C. Gonzalez, Pattern recognition principles, Coden Apmcc, 1992.
[43]J. Jantzen, Foundations of fuzzy control, West Sussex (England), 2007.
[44]Y. Yamamoto and X. Yun, “Coordinating locomotion and manipulation of a mobile manipulator, IEEE Trans. Automatic Control, vol. 39, no. 6, pp. 1326 - 1332, June 1994.
[45]Y. C. Chang and B. S. Chen, “Adaptive tracking control for nonholonomic Caplygin systems, IEEE Trans. Control Systems Technology, vol. 10, no. 1, pp. 96-104, 2002.
[46]J.A. Farrell and M. M. Polycarpou, Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, New York: Wiley, 2006.