Image De-Noising Method with Noise Control Materials Based on Wavelet Transform and Wiener Filter

Yuan Mei Wang; Tao Li

doi:10.4028/www.scientific.net/AMR.459.219

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 459Image De-Noising Method with Noise Control...

Image De-Noising Method with Noise Control Materials Based on Wavelet Transform and Wiener Filter

Abstract:

When image with Gaussian white noise being de-noised by wavelet threshold, there are some problems such as blurring and the loss of details of edges of image. To solve above problems, image de-noising method based on wavelet transform and Wiener filtering is proposed in the paper, first using wavelet threshold to de-noise, and then using Wiener filter to smooth the image so as to get high image quality. Experimental results show that this algorithm on de-noising proposed not only can effectively suppress Gaussian white noise, but also can well preserve the details of image edges

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Periodical:

Advanced Materials Research (Volume 459)

Pages:

219-223

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.459.219

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Online since:

January 2012

Authors:

Yuan Mei Wang, Tao Li

Keywords:

Image De-Noising, Noise Control Materials, Wavelet Threshold, Wavelet Transform (WT), Wiener Filter

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References

[1] Cui xiaojie.: The Application of Wiener Filtering. In: Master thesis Chang'an University, Institute of Geological Engineering and Surveying and Mapping. pp.4-13(2006).

Google Scholar

[2] GUO Shui-xia; TANG Yong-jun. : Extension and application of Wiener filtering in image processing . In: Computer Engineering and Applications, vol. 44(14). pp.178-180(2008).

Google Scholar

[3] Chen wufan. : Wavelet analysis and its application in image processing[M]. Beijing: Science Press. pp.156-157(2004).

Google Scholar

[4] Dong Changhong. : Principle and application of the Matlab wavelet analysis toolbox[M]. Beijing: National Defense Industry Press (2004).

Google Scholar

[5] Cheng lizhi, wang hongxia, luo yong. Wavelet Theory and Application. [M]. beijing: Science Press. pp.297-310(2004).

Google Scholar

[6] Zhang Ming, Gunturk B K. : Multiresolution bilateral filtering for image de-noising.J. In: IEEE Transactions on Image Processing. vol. 17(12)pp.2324-2333(2008).

DOI: 10.1109/tip.2008.2006658

Google Scholar

[7] Zhang Bo, Fadili J M, Starck J.: Wavelets, ridgelets, and curvelets for Poisson noise removal.J. In: IEEE Transactions on Image Processing. vol. 17(7)pp.1093-1108(2008).

DOI: 10.1109/tip.2008.924386

Google Scholar

[8] Pan quan , zhang lei, meng jingli.: Wavelet filtering method and application[M]. Beijing: Tsinghua University Press(2005).

Google Scholar

[9] Goossens B, Pizurica A, Philips W.: Image de-noising using mixtures of projected Gaussian scale mixtures.J. In: IEEE Transactions on Image Processing. vol. 18(8)pp.1689-1702(2009).

DOI: 10.1109/tip.2009.2022006

Google Scholar