DocumentCode
2316061
Title
A new image denoising method based on the dependency wavelet coefficients
Author
Zhang, Er-hu ; Huang, Shu-Ying
Author_Institution
Dept. of Inf. Sci., Xi´´an Univ. of Technol., China
Volume
6
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
3841
Abstract
The denoising of a natural image corrupted by noise is a classical problem in signal processing. A new method for image denoising is discussed. The bivariate shrinkage function based on the dependency between wavelet coefficients is derived from Bayesian maximum a posterior (MAP) estimation theory and applied to image denoising. The performance of the method is compared with that of the conventional soft thresholding technique. Experimental results show the method is satisfying in noise suppression, preserving edges and details.
Keywords
Bayes methods; belief networks; image denoising; maximum likelihood estimation; wavelet transforms; Bayesian maximum a posterior estimation theory; bivariate shrinkage function; dependency wavelet coefficient; image denoising method; noise suppression; Bayesian methods; Estimation theory; Gaussian noise; Image denoising; Information science; Noise reduction; Wavelet analysis; Wavelet coefficients; Wavelet domain; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1380505
Filename
1380505
Link To Document