Poisson-gaussian denoising using the exact unbiased inverse of the generalized anscombe transformation
Markku J. Makitalo, Alessandro Foi · 2012
The characteristic errors of many digital imaging devices can be modelled as Poisson-Gaussian noise, the removal of which can be approached indirectly through variance stabilization. The generalized Anscombe transformation (GAT) is commonly used for stabilization, but rigorous studies regarding its unbiased inverse transformation have been neglected. We introduce the exact unbiased inverse of the GAT, show that it is of essential importance for ensuring accurate denoising, and demonstrate that our approach leads to state-of-the-art results. This paper generalizes our earlier work, in which we presented an exact unbiased inverse of the Anscombe transformation for the case of pure Poisson noise removal.