Whiteness-based bilevel learning of regularization parameters in imaging
Carlo Santambrogio Monica Pragliola Alessandro Lanza Marco Donatelli Luca Calatroni
We consider an unsupervised bilevel optimization strategy for learning
regularization parameters in the context of imaging inverse problems in the
presence of additive white Gaussian noise. Compared to supervised and
semi-supervised metrics relying either on the prior knowledge of reference data
and/or on some (partial) knowledge on the noise statistics, the proposed
approach optimizes the whiteness of the residual between the observed data and
the observation model with no need of ground-truth data.We validate the
approach on standard Total Variation-regularized image deconvolution problems
which show that the proposed quality metric provides estimates close to the
mean-square error oracle and to discrepancy-based principles.