Locally adaptive multiscale Bayesian method for image denoising based on bivariate normal inverse Gaussian distributions

Recently, the use of wavelet transform has led to significant advances in image denoising applications. Among wavelet based denoising approaches, Bayesian techniques give more accurate estimates. Considering interscale dependencies, these estimates become closer to the original image. In this context, the choice of an appropriate model for wavelet coefficients is an important issue. The performance can also be improved by estimating model parameters in a local neighborhood. In this paper, we introduce a spatially adaptive MMSE-based Bayesian estimator using bivariate normal inverse Gaussian (NIG) distribution. The NIG distribution can model a wide range of processes, from heavy-tailed to less heavy-tailed processes. Exploiting this new statistical model in the dual-tree complex wavelet domain, we achieved state-of-the-art performance among related recent denoising approaches, both visually and in terms of peak signal-to-noise ratio (PSNR).