Wavelet Image Denoising by Threshold Optimization Based on Genetic Algorithm

Shuang Ping Zhao; Xiang Wei Li; Jing Hong Xing; Yan Wen Ye

doi:10.4028/www.scientific.net/AMR.186.337

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 186Wavelet Image Denoising by Threshold Optimization...

Wavelet Image Denoising by Threshold Optimization Based on Genetic Algorithm

Abstract:

This paper presents a wavelet image denoising method by Threshold optimal based on wavelet transform and genetic algorithm (GA). First, using wavelet transition to a original signal and selecting a wavelet and a level of wavelet decomposition, Then the optimized thresholds of every level of wavelet decomposition will be obtained by genetic algorithms. The high coefficients at every level will be quantized. At last, inverse transition of the coefficients will be processed and we will get the final signals. An optimal image threshold using Genetic Algorithm is proposed. Compared with traditional threshold methods, the proposed method has advantages that it can implement quickly optimal threshold and have good capability and stabilization. The results show that using the proposed method can obtain satisfactory denoising effect.

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Periodical:

Advanced Materials Research (Volume 186)

Pages:

337-341

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.186.337

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Online since:

January 2011

Authors:

Shuang Ping Zhao, Xiang Wei Li, Jing Hong Xing, Yan Wen Ye

Keywords:

Genetic Algorithm (GA), Image De-Noising, Threshold, Wavelet Transform (WT)

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References

[1] Gonzalez.R. C and. Wood R. E, in: Digital Image Processing, 2nd ed . New Jersey Prentice Hall, (2002).

Google Scholar

[2] MALLATS. A: Wavelet Tour of Signal Processing, London: Academic Press (1998).

Google Scholar

[3] M. Grangetto, E. Magli (2002): Optimisation and Implementation of theInteger Wavelet Transfonnfor Image Coding, IEEE Transactions on Image Processing, vol. 11, No. 6, pp.596-604.

DOI: 10.1109/tip.2002.1014991

Google Scholar

[4] M.D. Adams (2002): reversible integer to integer wavelet transfonns for image coding,M.A. Sc. thesis, Univ. Victoria, Victoria, Canada.

Google Scholar

[5] Donoho D L: De-noising by soft-thresholding, IEEE Transinform Theory, 1995, 41(3), pp.613-627.

DOI: 10.1109/18.382009

Google Scholar

[6] Xiaobing Ren: An Optimal Image Thresholding using Genetic Algorithm. 2009 International Forum on Computer Science-Technology and Applications, 2009, (48), pp.169-172.

DOI: 10.1109/ifcsta.2009.48

Google Scholar

[7] V. Thavavel, R. Murugesan: GA-Based Adaptive Wavelet Denoising of Low-Dose Medical Images, International Conference on Computational Intelligence and Multimedia Applications 2007, 2007. (301), p.48.

DOI: 10.1109/iccima.2007.301

Google Scholar