An Wavelet Image Automatic Threshold Selection Denoising Method

Shuang Ping Zhao; Xiang Wei Li; Jing Hong Xing; Gong Zheng

doi:10.4028/www.scientific.net/AMR.482-484.780

Paper Titles

The Process Optimization of EFO for Copper Wire Bonding
p.763

Portable Position Sensitive Detector with Outdoor Environment Resolution
p.767

The Research and Performance Simulation of C-WQPSK Modulation
p.771

Simulation Research on Stress – Damage Law of the Blood Cells
p.776

An Wavelet Image Automatic Threshold Selection Denoising Method
p.780

Topological Optimization on the Board which above Flexible Clamping Stent Fixture
p.784

Study and Application of DPRAM Communication Technology in Engraving CNC System
p.788

Research and Simulation on Drawing Force of the Tripod Type Universal Coupling
p.792

The Research Status and Existing Problems of Microscopic Visual Servoing Based on Image
p.796

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 482-484An Wavelet Image Automatic Threshold Selection...

An Wavelet Image Automatic Threshold Selection Denoising Method

Abstract:

A wavelet image automatic threshold selection denoising method based on wavelet transform and genetic algorithm. Firstly, wavelet transition is introduced to an original signal and selecting a wavelet and a level of wavelet decomposition. Secondly, the automatic thresholds of every level of wavelet resolved are obtained by using genetic algorithms. At the same time, the high coefficients of every level were quantized. Thirdly, inverse transition of the coefficients was processed and achieves the final resulting signals. Compared to traditional threshold methods, the proposed method has advantages that it can implement quickly optimal threshold and good capability and stabilization. The final experiments results show that using the proposed algorithm can obtain satisfactory denoising effect.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 482-484)

Pages:

780-783

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.482-484.780

Citation:

Cite this paper

Online since:

February 2012

Authors:

Shuang Ping Zhao, Xiang Wei Li, Jing Hong Xing, Gong Zheng

Keywords:

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

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

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] Donoho D L and Johnstone I M.Ideal spatial adaptation via wavelet shrinkage.Biometrika,1994,81(3):425-455

DOI: 10.1093/biomet/81.3.425

Google Scholar

[7] D.T. Kuan, A. A. Sawchuk,T. C. Strand, andP. Chavel, "Adaptive noise smoothing filter for images with signaldependent noise", IEEE Trans. PAMI, 1985,vol. 7, pp.165-177.

DOI: 10.1109/tpami.1985.4767641

Google Scholar

[8] 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

[9] 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