IMAGE DENOISING IN COMPLEX WAVELET DOMAIN USING A MIXTURE OF BIVARIATE LAPLACIAN DISTRIBUTIONS WITH LOCAL PARAMETERS

摘要

The employed distribution for the noise-free data and the accuracy of the involving parameters play key roles in the performance of estimators, such as maximum a posteriori (MAP). In this paper, we select a proper model for the distribution of wavelet coefficients and present a new image denoising algorithm. We model the wavelet coefficients in each subband with a mixture of two bivariate Laplacian probability density functions (pdfs) using local parameters for the mixture model. This pdf simultaneously allows capturing the heavy-tailed nature of the wavelet coefficients, exploiting the interscale dependencies in the adjacent scales and modeling the intrascale dependencies of coefficients in each subband. We propose a MAP estimator for image denoising using this mixture model and the estimated local parameters. We compare our method with other techniques employing mixture pdfs such as univariate Laplacian mixture model with local parameters and bivariate Laplacian mixture model without local parameters. Despite the simplicity of our proposed method, the simulation results reveal that it outperforms these techniques and several recently published methods both visually and in terms of peak-signal-to-noise-ratio (PSNR).