Section: New Results

Statistical aggregation methods for image denoising and estimation

Participants : Charles Kervrann, Frédéric Lavancier.

We have already proposed a general statistical aggregation method which combines image patches denoised with several commonly-used algorithms [10] . We showed that weakly denoised versions of the input image obtained with standard methods, can serve to compute an efficient patch-based aggregated estimator. In our approach, the Stein's Unbiased Risk Estimator (SURE) is used to evaluate each denoised candidate image patch and to compute the exponential weighted aggregation (EWA) estimator. This year, we adapted this framework (PEWA) to denoise images corrupted by mixed Gaussian-Poisson in 2D fluorescence image sequences.

In this range of work, we have also introduced in [24] a general method to combine estimators in order to produce a better estimate. From a theoretical point of view, we proved that this method is optimal in some sense. It is illustrated on standard statistical problems in parametric and semi-parametric models where the averaging estimator outperforms the initial estimators in most cases. As part of an on-going work, we are applying this method to improve patch-based image denoising algorithms.

Reference:  [24]

Collaborators: Paul Rochet (Laboratoire de Mathématiques Jean Leray (LMJL), University of Nantes).