An Image De-Noising Method Using Directions of Wavelet Decomposition Sub-Bands

Zheng Cai; Shao Hua Tao

doi:10.4028/www.scientific.net/AMM.130-134.3058

Paper Titles

Application of Numerical Simulation for Tire Rolling Resistance Measuring Method
p.3041

Core Technologies Analysis for End-Hopping System
p.3046

Numerical Study on Flow Characteristics in a Stirring Vessel
p.3050

Research of Scalability Model Based on Cloud Storage System
p.3054

An Image De-Noising Method Using Directions of Wavelet Decomposition Sub-Bands
p.3058

An Ant Colony Optimization for the Composite SaaS Placement Problem in the Cloud
p.3062

Image Compression and Transmission System for Digital Down-Hole Television
p.3068

The Study on the Modeling Method of Complex R and D System
p.3072

Topology Optimization of Compliant Mechanisms Using Level Set Method without Re-Initialization
p.3076

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134An Image De-Noising Method Using Directions of...

An Image De-Noising Method Using Directions of Wavelet Decomposition Sub-Bands

Abstract:

In this paper, an image de-noising method using directions of wavelet decomposition sub-bands is proposed. Wavelet coefficients are correlated in a small area, and the wavelet transform uses wavelet coefficients in three directions to describe image information, so in each sub-band the proposed method only calculates neighboring wavelet coefficients in a certain direction rather than in eight directions. Compared with other wavelet de-noising methods, the proposed method can achieve higher peak signal to noise ratio.

You might also be interested in these eBooks

Mechanical and Electronics Engineering III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

3058-3061

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.3058

Citation:

Cite this paper

Online since:

October 2011

Authors:

Zheng Cai, Shao Hua Tao

Keywords:

Image De-Noising, Neighboring Wavelet Coefficients, Wavelet Transform (WT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] L. Xue-zhang and H. Jia-xing: Wavelet Analysis (National Defense Industry Press, Beijing, 2005).

Google Scholar

[2] S.G. Chang, B. Yu and M. Vetterli: Adaptive wavelet thresholding for image denoising and compression, IEEE Trans. Image Processing, vol. 9 (2000), pp.1532-1546.

DOI: 10.1109/83.862633

Google Scholar

[3] G.Y. Chen, T.D. Bui and A. Krzyzak: Image denoising using neighbouring wavelet coefficients, 2004 International Conference on Acoustics, Speech, and Signal Processing.

DOI: 10.1109/icassp.2004.1326408

Google Scholar

[4] L. Sendur and I.W. Selesnick: Bivariate shirnkage with local variance estimation, IEEE Signal Processing Letters, vol. 9 (2002), pp.438-441.

DOI: 10.1109/lsp.2002.806054

Google Scholar

[5] L. Sendur and I.W. Selesnick: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency, IEEE Trans. Image Processing, vol. 50 (2002), pp.2744-2756.

DOI: 10.1109/tsp.2002.804091

Google Scholar

[6] S. Jamal, H. M. Mohammad and F. Karim: A new wavelet-based fuzzy single and multi-channel image denoising, Image and Vision Computing, vol. 28 (2010), pp.1611-1623.

DOI: 10.1016/j.imavis.2010.04.004

Google Scholar

[7] Y. Hawwar and A. Reza: Spatially adaptive multiplicative noise image denoising technique, IEEE Trans. Image Processing, vol. 11 (2002), pp.1397-1404.

DOI: 10.1109/tip.2002.804526

Google Scholar

[8] W. B. Yu, Y. Wang, B. M. Pang and Z. J. Xu: An improved threshold denoising algorithm based on inter-scale dependency of wavelet, 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, vol. 4, pp.231-233.

DOI: 10.1109/cmce.2010.5610165

Google Scholar