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Conventional image interpolation algorithm always introduces the the blur and jagged edges. To solve this problem, an improved adaptive image interpolation algorithm with using membership function is proposed in this paper. Fuzzy logic is used to obtain the membership function with the local characteristics of the gradient and phase angle. The first step is to correct the special distance of interpolated pixels along one dimension in the basis of local asymmetry features and the membership function, and then to convert the corrected distance of one dimension into two dimensions, applying the corrected distance to conventional image interpolation algorithm. Experimental results demonstrate that the improved algorithm can produce better results in regard to the signal-to-nosie ratio and succeed in preserving interpolation image edges in various directions.
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Keywords:
- membership function /
- distance /
- image interpolation
[1] Xiang L Z, Xing D, Guo H, Yang S H 2009 Acta Phys. Sin. 58 4610(in Chinese) [向良忠、 邢 达、 郭 华、 杨思华 2009 58 4610]
[2] John H M, Kurtis D F 2009 Numerical Methods Using MATLAB (Beijing: Publishing House of Electronics Industry) p144
[3] Thévenaz P, Elu T B, Unser M 2000 IEEE Transactions on Medical Image 7 739
[4] Natale F D, Desoli G S, Giusto D D 1993 IEEE Electronics Lett. 9 1638
[5] Keys R G 1986 IEEE Trans. Sign. Proces 29 1153
[6] Zhang M Y, Wang X T, Xu X G 2009 Journal of Image and Graphics 14 853 (in Chinese) [张美玉、 王孝通、 徐晓刚 2009 中国图像图形学报 14 853]
[7] Shuai Y, Massahide A A T, Masayuki K 2005 Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems p85
[8] Wang Q, Tan J Q, Hu M 2007 Journal of Computer-aided Design&Computer Graphics 19 1348 (in Chinese)[王强、 檀结庆、 胡 敏 2007 计算机辅助设计与图形学学报 19 1348]
[9] Gonzalez R C, Woods R E 1992 Digital Image Processing (Massachusetts:Addison-Wesley Publishing Company) p402
[10] Hwang J W, Lee H S 2004 IEEE Trans. Sign. Proces. Lett. 3 359
[11] Sendur L, Selesnick I W 2002 IEEE Trans. Sign. Proces. 50 2744
[12] Bezdek J C 1981 Pattern Recognition with Fuzzy Objective Function Algorithms (New York: Plenum Press) p39
[13] Liang Y M, Zhai H C, Mu G G 2002 Acta Phys. Sin. 51 2671(in Chinese) [梁艳梅、 翟宏琛、 母国光 2002 51 2671]
[14] Zong X P, Xu Y, Dong J T 2006 Acta Phys. Sin. 55 3223 (in Chinese) [宗晓萍、 徐 艳、 董江涛 2006 55 3223]
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[1] Xiang L Z, Xing D, Guo H, Yang S H 2009 Acta Phys. Sin. 58 4610(in Chinese) [向良忠、 邢 达、 郭 华、 杨思华 2009 58 4610]
[2] John H M, Kurtis D F 2009 Numerical Methods Using MATLAB (Beijing: Publishing House of Electronics Industry) p144
[3] Thévenaz P, Elu T B, Unser M 2000 IEEE Transactions on Medical Image 7 739
[4] Natale F D, Desoli G S, Giusto D D 1993 IEEE Electronics Lett. 9 1638
[5] Keys R G 1986 IEEE Trans. Sign. Proces 29 1153
[6] Zhang M Y, Wang X T, Xu X G 2009 Journal of Image and Graphics 14 853 (in Chinese) [张美玉、 王孝通、 徐晓刚 2009 中国图像图形学报 14 853]
[7] Shuai Y, Massahide A A T, Masayuki K 2005 Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems p85
[8] Wang Q, Tan J Q, Hu M 2007 Journal of Computer-aided Design&Computer Graphics 19 1348 (in Chinese)[王强、 檀结庆、 胡 敏 2007 计算机辅助设计与图形学学报 19 1348]
[9] Gonzalez R C, Woods R E 1992 Digital Image Processing (Massachusetts:Addison-Wesley Publishing Company) p402
[10] Hwang J W, Lee H S 2004 IEEE Trans. Sign. Proces. Lett. 3 359
[11] Sendur L, Selesnick I W 2002 IEEE Trans. Sign. Proces. 50 2744
[12] Bezdek J C 1981 Pattern Recognition with Fuzzy Objective Function Algorithms (New York: Plenum Press) p39
[13] Liang Y M, Zhai H C, Mu G G 2002 Acta Phys. Sin. 51 2671(in Chinese) [梁艳梅、 翟宏琛、 母国光 2002 51 2671]
[14] Zong X P, Xu Y, Dong J T 2006 Acta Phys. Sin. 55 3223 (in Chinese) [宗晓萍、 徐 艳、 董江涛 2006 55 3223]
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