Cited By
View all- Sheta A(2017)Restoration of Medical Images Using Genetic Algorithms2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)10.1109/AIPR.2017.8457940(1-8)Online publication date: Oct-2017
Fast image filtering via adaptive noise detection
Pages 95 - 99
Abstract
This work develops a spatially white Gaussian noise detection algorithm for blind image filtering. First, the noise component as well as the noise level are estimated by using the local statistics from an observed degraded image. Then, the first order Markov Random Field is effectively used to control the noise detection performance. Finally, pixels, the adaptive weighted filter with the resizable patches is adopted to restore the detected noisy pixels. Numerous simulations have been conducted to demonstrate the effectiveness of the proposed method.
References
[1]
Gonzalez, R. C., Wood, R. E., 2007. Digital Image Processing. Prentice Hall.
[2]
Lyndell M. K. Comprehending the Central Limit Theorem. ACM SIGCSE. 20, 2 (June 1988), 20--25. DOI= http://dx.doi.org/10.1145/45202.45208
[3]
Arce, G. R. 2004. Nonlinear Signal Processing: A Statistical Approach. Wiley.
[4]
Nodes, T. A., Gallagher, N. C. 1982. Mean filters: Some modifications and their properties. IEEE Transactions on Acoustics, Speech and Signal Processing. 30, 5 (October 1982), 739--746. DOI= http://dx.doi.org/10.1109/TASSP.1982.1163951
[5]
Brownrigg. D. The weighted median filter. Communications of the ACM. 27, 8 (Aug. 1984), 807--818. DOI=http://dx.doi.org/10.1145/358198.358222
[6]
Sylvain. P., Pierre. K., Jack. T., Fredo. D. A gentle introduction to bilateral filtering and its applications. ACM SIGGRAPH (May 2008). 1--50. DOI=http://dx.doi.org/10.1145/1401132.1401134
[7]
Lee. J. H., Nam. J. H. Adaptive noise reduction algorithm based on statistical hypotheses tests. IEEE Transactions on Consumer Electronics. 54, 3 (August 2008), 1406--1414. DOI= http://dx.doi.org/10.1109/TCE.2008.4637634
[8]
Vijaykumar. V. R., Kanagasa. P. Fast and efficient algorithm to remove Gaussian noise in digital images. International Journal of Computer Science. 37, 1 (March 2010), 78--82.
[9]
Olsen. S. I. Noise variance estimation in images. Computer Vision, Graphics and Image Processing. 55, 4 (April 1993). DOI= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.7122
[10]
Stan Z. L. 2009. Markov Random Field Modeling in Image Analysis. Springer.
[11]
Wang. Z., Bovik. A. C., Simmoncelli. E. P. Image quality assessment: from error visibility to structural similarity. IEEE Transaction on Image Processing. 13, 4. (April 2004), 600--612. DOI=http://dx.doi.org/10.1109/TIP.2003.819861
[12]
Li. H. C., Fan. P. Z., Khan. M. K. Context-Adaptive Anisotropic Diffusion for image denoising. IEEE Electronics letters. 48, 14 (July 2012), 827--829. DOI= http://dx.doi.org/10.1049/el.201
Index Terms
- Fast image filtering via adaptive noise detection
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Published In
October 2014
386 pages
ISBN:9781450330602
DOI:10.1145/2663761
- Conference Chairs:
- Chao Lu,
- Esmaeil Nadimi,
- Program Chairs:
- Sung-Ryul Kim,
- Wei Wang
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Published: 05 October 2014
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RACS '14: International Conference on Research in Adaptive and Convergent Systems
October 5 - 8, 2014
Maryland, Towson
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View all- Sheta A(2017)Restoration of Medical Images Using Genetic Algorithms2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)10.1109/AIPR.2017.8457940(1-8)Online publication date: Oct-2017
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