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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1380505
