Two-Stage Local Polynomial Regression Method for Image Heteroscedastic Noise Removal

Li Yun Su; Chun Hua Wang

doi:10.4028/www.scientific.net/AMR.860-863.2936

Paper Titles

Topic Detection and Tracking Method for Tibetan Network
p.2914

Study on Pulse-Signal Detection Methods Using Wavelet Transform and Hilbert Huang Transform
p.2918

Pitch Perception of Medium-Rank Harmonic Complex Tones Based on Temporal Analysis
p.2924

The Application Research on Logistics of Dangerous Chemical Cargoes Based on the Technology of Internet of Things
p.2929

Two-Stage Local Polynomial Regression Method for Image Heteroscedastic Noise Removal
p.2936

Triple Wronskian Representation of the Multi-Soliton Solutions for a Coupled GMNLS Equations
p.2940

Technology of Image Compression Coding Based on Walsh Transform
p.2946

Semantic Analysis Investigation of Data Mining Technology in Network Forum
p.2950

Research on Intelligent Regulation of Ship-to-Ship Crude Oil Transfer at Sea Based on Internet of Things
p.2954

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863Two-Stage Local Polynomial Regression Method for...

Two-Stage Local Polynomial Regression Method for Image Heteroscedastic Noise Removal

Abstract:

In this paper, we introduce the extension of local polynomial fitting to the linear heteroscedastic regression model and its applications in digital image heteroscedastic noise removal. For better image noise removal with heteroscedastic energy, firstly, the local polynomial regression is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. Due to non-parametric technique of local polynomial estimation, we do not need to know the heteroscedastic noise function. Therefore, we improve the estimation precision, when the heteroscedastic noise function is unknown. Numerical simulations results show that the proposed method can improve the image quality of heteroscedastic noise energy.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

2936-2939

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.2936

Citation:

Cite this paper

Online since:

December 2013

Authors:

Li Yun Su, Chun Hua Wang

Keywords:

Heteroscedastic Noise, Local Polynomial Regression, Noise Removal

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] T. Amemiya, Journal of Econometrics, vol. 6, pp.365-370, (1997).

Google Scholar

[2] Q. X. He and M. Zheng, Local Polynomial Regression for Heteroscedaticity in the Simple Linear Model, Systems Engineering-Theory Methodology Applications, vol. 12, no. 2, 153-156, (2003).

Google Scholar

[3] L. Galtchouk and S. Pergamenshchikov, Journal of Nonparametric Statistics, vol. 21, no. 1, pp.1-18, (2009).

Google Scholar

[4] J. Fan, The Annals of Statistics, vol. 21, pp.196-216, (1993).

Google Scholar

[5] M. Pandya, K. Bhatt, and P. Andharia, International Journal of Quality, Statistics, and Reliability, vol 2011, Article ID 357814, 9 pages, (2011).

Google Scholar

[6] L. Y. Su, Computers & Mathematics with Applications, vol. 59, no. 2, pp.737-744, (2010).

Google Scholar

[7] L. Y. Su, Discrete Dynamics in Nature and Society, vol. 2011, Article ID 930958, 11 pages, (2011).

Google Scholar

[8] Su Liyun and Li Fenglan, Mathematical Problems in Engineering, vol. 2010, Article ID 605241, 14 pages, (2010).

Google Scholar