Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29306


Select areas to restrict search in scientific publication database:
6646
A Novel Fuzzy Technique for Image Noise Reduction
Abstract:
A new fuzzy filter is presented for noise reduction of images corrupted with additive noise. The filter consists of two stages. In the first stage, all the pixels of image are processed for determining noisy pixels. For this, a fuzzy rule based system associates a degree to each pixel. The degree of a pixel is a real number in the range [0,1], which denotes a probability that the pixel is not considered as a noisy pixel. In the second stage, another fuzzy rule based system is employed. It uses the output of the previous fuzzy system to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Experimental results are obtained to show the feasibility of the proposed filter. These results are also compared to other filters by numerical measure and visual inspection.
Digital Object Identifier (DOI):

References:

[1] K. Arakawa, Fuzzy rule-based image processing with optimization, Fuzzy techniques in image processing, Springer-Verlag, 2000.
[2] D. Van De Ville, M. Nachtegael, D. Van der Weken, E. Kerre, W. Philips, and I. Lemahieu, Noise Reduction by Fuzzy Image Filtering, IEEE transactions on fuzzy systems, vol. 11, No. 4, August 2003.
[3] F.Farbiz, M. Menhaj, S. Motamedi, Fixed Point Filter Design for Image Enhancement using Fuzzy Logic, In Proc. IEEE, 1998.
[4] P. Liu and H. Li, Fuzzy techniques in image restoration research- A survey, International Journal of Computational Cognition, Volume 2, pp 131-149, June 2004.
[5] F. Russo and G. Ramponi, A fuzzy operator for the enhancement of blurred and noisy images, IEEE Trans. Image Processing, vol. 4, pp.1169-1174, August 1995.
[6] F. Russo, "Fire operators for image processing," Fuzzy Sets Syst., vol. 103, no. 2, pp. 265-275, 1999.
[7] C.-S. Lee, Y.-H. Kuo, and P.-T. Yu, Weighted fuzzy mean filters for image processing, Fuzzy Sets Syst., no. 89, pp. 157-180, 1997.
[8] C. Lee and Y. Kuo, Fuzzy Techniques in Image Processing. New York: Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and Soft Computing, ch. Adaptive fuzzy filter and its application to image enhancement, pp. 172-193.
[9] F. Farbiz and M. B. Menhaj, Fuzzy Techniques in Image Processing. New York: Springer-Verlag, 2000, vol. 52, Studies in Fuzziness and Soft Computing, ch. A fuzzy logic control based approach for image filtering, pp. 194-221.
[10] K. Arakawa, Median filter based on fuzzy rules and its application to image restoration, Fuzzy Sets Syst., pp. 3-13, 1996.
[11] Y.Choi and R. Krishnapuram, A robust approach to image enhancement based on fuzzy logic, IEEE Trans. Image Processing, vol. 6, no. 6, June 1997, p.808-825.
[12] A. Taguchi, A design method of fuzzy weighted median filters, In Proc. Third IEEE Int. Conf Image Processing, vol. 1,.1996 p.423-426.
[13] M. Muneyasu, Y. Wada, T. Hinamoto, Edge-preserving smoothing by adaptive nonlinear filters based on fuzzy control laws, In Proc. Third IEEE Int. Conf Image Processing,Vol.1,1996. pp. 785-788.
Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007