DocumentCode :
2942416
Title :
Multivariate statistical approach for image denoising
Author :
Cho, Dongwook ; Bui, T.D.
Author_Institution :
Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada
Volume :
4
fYear :
2005
fDate :
18-23 March 2005
Abstract :
In this paper, we derive the general estimation rule in the wavelet domain to obtain the denoised coefficients from the noisy image based on the multivariate statistical theory. We define a parametric multivariate generalized Gaussian distribution (MGGD) model which closely fits the actual distribution of wavelet coefficients in clean natural images. The multivariate model makes it possible to exploit the dependency between the estimated wavelet coefficients and their neighbours or other coefficients in different subbands. Also it can be shown that some of the existing methods based on statistical modeling are subsets of our multivariate approach. Our method could achieve high quality image denoising. Among the comparable image denoising methods using the same type of wavelet (esp. Daubechies 8) filter, our results produce comparatively higher peak signal to noise ratio (PSNR).
Keywords :
Bayes methods; Gaussian distribution; image denoising; maximum likelihood estimation; wavelet transforms; Bayesian estimation; Daubechies wavelet; MAP estimator; MGGD model; PSNR; denoised wavelet coefficients; high quality image denoising; multivariate generalized Gaussian distribution; multivariate statistical theory; natural images; peak signal to noise ratio; wavelet coefficient distribution; Bayesian methods; Computer science; Filters; Frequency domain analysis; Gaussian distribution; Image denoising; PSNR; Wavelet coefficients; Wavelet domain; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8874-7
Type :
conf
DOI :
10.1109/ICASSP.2005.1416077
Filename :
1416077
Link To Document :
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