Local bivariate cauchy distribution for video denoising in 3-D complex wavelet domain

In this paper, we present a new video denoising algorithm using bivariate Cauchy probability density function (pdf) with local scaling factor for distribution of wavelet coefficients in each subband. The bivariate pdf takes into account the statistical dependency among wavelet coefficients and the local scaling factor model the empirically observed correlation between the coefficient amplitudes. Using maximum a posteriori (MAP) estimator and minimum mean squared estimator (MMSE), we describe two methods for video denoising which rely on the bivariate Cauchy random variables with high local correlation. Because separate 3-D transforms, such as ordinary 3-D wavelet transforms (DWT), have artifacts that degrade their performance for denoising, we implement our algorithms in 3-D complex wavelet transform (DCWT) domain. In addition, we use our denoising algorithm in 2-D DCWT domain, where the 2-D transform is applied to each frame individually. The simulation results show that our denoising algorithms achieve better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).