Fuzzy Based Distance Correction Algorithm for Digital Image Interpolation

Article Preview

Abstract:

Image interpolation is one of the key technologies in image processing. A special distance correction algorithm based on membership function is proposed in this paper to complete image interpolation. Fuzzy logic is used to get 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, then convert the corrected distance of one dimension into two dimension, applying the corrected distance to conventional image interpolation algorithms. Experimental results demonstrate that this algorithm can produce better results in regard to the signal-to-nosie ratio and succeeds in preserving interpolation image edges of various directions.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1549-1554

Citation:

Online since:

February 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Xiang Liang-Zhong, Xing Da, Guo Hua, Yang Si-Hua. High resolution fast digital photoacoustic CT for breast cancer diagnosis[J], 2009. Vol. 58, No. 7. Acta Phys. Sin. 58: 4610-4617.

DOI: 10.7498/aps.58.4610

Google Scholar

[2] Qu Xun Zheng, Wang Guo Yu, Xu Jing. Image Interpolation Algorithm Based on Edge-sensitive Filtering[J]. Vol 23, No 83, 2007: 287-301.

Google Scholar

[3] Amin Behnad, Konstantinos. N. Plataniotis, Xiaolin Wu. Directional image interpolation with ANOVA methodology[J]. 2010 IEEE International Conference on Image Processing. 2010. 9: 2001-(2004).

DOI: 10.1109/icip.2010.5652904

Google Scholar

[4] Thévenaz P, Elu T B, UnserM. Interpolation revisited[J]. IEEE Transactions on Medical Image. 2000 7( 19): 739-758.

Google Scholar

[5] Usman Babawuro, Zou Beiji, Xu Bing. High Resolution Satellite Imagery Rectification Using Bi-linear Interpolation Method for Geometric Data Extraction, 2012 Second International Conference on Intelligent System Design and Engineering Application, 2012. 01: 1430-1434.

DOI: 10.1109/isdea.2012.457

Google Scholar

[6] Keys Robort G. Cubic convolution interpolation for digital image processing[J]. IEEE Transaction on Signal Processing. 1986(ASSP-29): 1153-1160.

DOI: 10.1109/tassp.1981.1163711

Google Scholar

[7] Meiyu Zhang, Xiaotong Wang and Xiaogang Xu. An Improved Adaptive Image Interpolation with Gradient Features. Journal of Image and Graphics. Vol 14, No 5 2009. 5: 853-858.

Google Scholar

[8] Max Mignotte. An Energy-Based Model for the Image Edge-Histogram Specification Problem, IEEE Transactions on Image Processing, 2012. 01, vol 21: 379-386.

DOI: 10.1109/tip.2011.2159804

Google Scholar

[9] Qiang Wang, Jieqing Tan, Min Hu. Image Zooming Based on Rational Interpolatory Spline[J], journal of computer-aided design&computer graphics, Vo l9, No 10, 2007: 1348-1351.

Google Scholar

[10] Gonzalez R C and Woods R E. Digital Image Processing[C]. 1992. (Massachusetts: Addison-Wesley Publishing Company) : 402.

Google Scholar

[11] Hwang Jung-Woo, Lee Hwang-Soo. Adaptive image interpolation based on local gradient features[J]. IEEE Signal Processing Letters, 2004, 3(11): 359-362.

DOI: 10.1109/lsp.2003.821718

Google Scholar

[12] Bezdek J C 1981 Pattern Recognition with Fuzzy Objective Function Algorithms (New York: Plenum Press) : 39.

Google Scholar