2023
DOI: 10.48550/arxiv.2302.10056
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Bilevel learning of regularization models and their discretization for image deblurring and super-resolution

Abstract: Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven methods. Upon suitable parametrization of the desired quantities of interest (e.g., regularization terms or discretization filters), such approach computes optimal parameter values by solving a nested optimization problem where the variational model acts as a constraint. In this work, we consider two different use cases of bilevel l… Show more

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References 35 publications
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