Conference Proceeding

Image Denoising Based on A Mixture of Bivariate Laplacian Models in Complex Wavelet Domain

Dept. of Biomed. Eng., Amirkabir Univ. of Technol., Tehran
11/2006; DOI:10.1109/MMSP.2006.285344 In proceeding of: Multimedia Signal Processing, 2006 IEEE 8th Workshop on
Source: IEEE Xplore

ABSTRACT Recently, it has been shown that algorithms exploiting dependencies between coefficients for modeling probability density function (pdf) of wavelet coefficients, could achieve better results for image denoising in wavelet domain compared with the ones based on the independence assumption. In this context, we design a bivariate maximum a posteriori (MAP) estimator which relies on a mixture of bivariate Laplacian models. This model not only is bivariate but also is mixture and therefore, using this new statistical model, we are able to better capture heavy-tailed natures of the data as well as the interscale dependencies of wavelet coefficients. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR)

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Keywords

algorithms exploiting dependencies
 
bivariate Laplacian models
 
bivariate maximum
 
capture heavy-tailed natures
 
coefficients
 
independence assumption
 
modeling probability density function
 
new statistical model
 
peak signal-to-noise ratio
 
posteriori
 
proposed technique
 
PSNR
 
published methods
 
simulation results
 
wavelet coefficients