A Bayesian approach for natural image denoising
Jordi Salvador, Malte Borsum, Axel Kochale · 2013
This article presents a new method for estimating the latent noiseless version of an observed image corrupted by additive noise. This method stems from classical models in parametric denoising and extends them by modeling the likelihood term, estimating adaptive image priors and automatically choosing an adaptive equivalent to the typically hand-tuned regularization constant. The proposed method introduces a possible path to overcome the limitations of current parametric denoising algorithms and provides a competitive alternative to powerful non-parametric ones. The experimental results show how our method adapts better to different noise types than state-of-the-art parametric and non-parametric algorithms.