Whiteness-based bilevel learning of regularization parameters in imaging
<|reference_start|>Whiteness-based bilevel learning of regularization parameters in imaging: 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.<|reference_end|>
@article{santambrogio2024whiteness-based,
    title={Whiteness-based bilevel learning of regularization parameters in imaging},
    author={Carlo Santambrogio, Monica Pragliola, Alessandro Lanza, Marco
  Donatelli, Luca Calatroni},
    journal={arXiv preprint arXiv:2403.07026},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07026},
    primaryClass={math.OC cs.LG eess.IV}
}