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Article:Image Enhancement and De-noising by Diffusion based Singular Value Decompositiong
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 32 - Number 8 |
| Year of Publication: 2011 |
| Authors: Nafis uddin Khan, K. V. Arya, Manisha Pattanaik |
10.5120/3923-5538
|
Nafis uddin Khan, K. V. Arya, Manisha Pattanaik . Article:Image Enhancement and De-noising by Diffusion based Singular Value Decompositiong. International Journal of Computer Applications. 32, 8 ( October 2011), 14-22. DOI=10.5120/3923-5538
Abstract
This paper proposes an approach for enhancement and de-noising of the images having fine edges and homogeneous smooth regions by using singular value decomposition filtering technique on the diffused image subspaces. The existing singular value decomposition based image de-noising technique faces the problem of selecting the optimum threshold parameter for separation of noise subspace and noise-free image subspace. The proposed approach is a two stage process in which the diffused versions of the image are generated in the first stage using partial differential equation based linear isotropic diffusion to smooth the homogeneous regions and the inverse heat diffusion method for enhancement of the edge features. In the next stage, singular value decomposition is applied on the two oppositely featured diffused versions of the image with fixed threshold individually to remove noise. Experimental results were compared with respect to recently developed singular value decomposition method with minimum energy model and traditional block based singular value decomposition filtering method in terms of signal to noise ratio which shows that the proposed method is efficient for image enhancement as well as de-noising.
References
- Kother Mohideen, Arumuga Perumal, Krishnan and Mohamed Sathik, 2011 “Image de-noising and enhancement using multiwavelet with hard threshold in digital mammographic images,” International Arab Journal of e-Technology, vol. 2, no. 1, 49-55.
- Md. Foisal Hossain, Mohammad Reza Alsharif, Katsumi Yamashita, 2008 “Image de-noising using adaptive singular value decomposition in nonsubsampled contourlet transform domain,” ARPN Journal of Engineering and Applied Sciences, vol. 3, no. 3, 31-35.
- Zujun Hou, 2003 “Adaptive singular value decomposition in wavelet domain for image de-noising,” Elsevier: Pattern Recognition, vol. 36, 1747-1763.
- Sunil K. Jha, Yadava R. D. S., 2011 “De-noising by singular value decomposition and its application to electronic nose data processing,” IEEE Sensors Journal, vol. 11, no. 1, 35-44.
- Shin-Min Chao, Du-Ming Tsai, 2010 “An improved anisotropic diffusion model for detail and edge preserving smoothing,” Pattern Recognition Letters, vol. 31, 2012–2023.
- Yong-le L. V., Lang Rong-ling, Liang Jia-cheng, 2009 “Decision of threshold for singular value decomposition filter based on SNR’s empirical value,” Journal of Application Research of Computers, TN713.
- Priyam Chatterjee, 2007 “De-noising using the K-SVD method,” EE 264-Image Processing and Reconstruction, Spring 2007.
- Andrews H. C., Patterson C. L., 1976 “Singular value decompositions and digital image processing,” IEEE Transactions on Accoustic Speech, Signal Processing, ASSP-24, 26–53.
- Konstantinides K., Natarajan B., Yovanof G. S., 1997 “Noise estimation and filtering using block-based singular value decomposition,” IEEE Transactions on Image Processing, vol. 6, no. 3, 479–483.
- Perona P. and Malik J., 1987 “Scale space and edge detection using anisotropic diffusion,” IEEE Computer Society Workshop on Computer Vision, 16-27.
- Perona P. and Malik J., 1990 “Scale space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 8, 629-639.
- Shin-Min Chao, Du-Ming Tsai, 2010 “Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images,” Elsevier: Pattern Recognition, vol. 43, 1917-1931.
- Dai Fang, Zhang Nanning, Xiu Jianru, 2008 “Image smoothing and sharpening based on nonlinear diffusion equation,” Elsevier: Signal Processing, vol. 88, 2850-2855.
- Qiang Chen, Philippe Montesinos, quan Sen Sun De Shen Xia, 2010 “Ramp preserving Perona-Malik method,” Elsevier: Signal Processing, vol. 90, 1963-1975.
- Ardeshir Goshtasby, Martin Satter, 2008 “An adaptive window mechanism for image smoothing,” Computer Vision and Image Understanding, no. 111, 155–169.
- Zhang Zhijia, Zhang Haifeng, Sang Haifeng and Yuan Weigi, 2008 “SVD based image de-noising with the minimum energy model,” 2008 International Congress on Image and Signal Processing, CISP 2008, 411-415.
- Mohsen Nikpour, Nadernejad E., Ashtiani H. and Hassanpour H., 2009 “Using PDEs for noise reduction in time series,” International Journal of Computing and ICT Research, vol. 3, no.1, 42-48.
- Hassanpour H., 2007 “Improved SVD technique for enhancing time frequency representation of signals,” IEEE International Symposium on Circuits and Systems (ISCAS), New Orleans, USA, 1819-1822.
- Van Der Veen A. J., Deprettere E.F., Lee Swindlehurst A., 1993 “Subspace-based signal analysis using singular value decomposition,” Proceedings of the IEEE, vol. 81, 1277–1308.
- Konstantinides K., Yao K., 1988 “Statistical analysis of effective singular values in matrix rank determination,” IEEE Transactions on Accoustic. Speech, Signal Processing, vol. 36, 757–763.
- Yanmin He, Tao Gan, Wufan Chen, and Houjun Wang, 2011 “Adaptive de-noising by singular value decomposition,” IEEE Signal Processing Letters, vol. 18, no. 4, 215-218.
- Nafis uddin Khan, Arya K. V., Manisha Pattanaik, 2011 “A robust PDE based image de-noising method,” 2011 3rd International Conference on Signal Acquisition and Processing (ICSAP 2011), Singapore, vol. 1, 136-139.
- Rafael C. Gonzalez, Richard E. Woods, 2008 “Digital image processing,” 3rd Edition, Pearson Prentice Hall.
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