Optimality Conditions for Bilevel Imaging Learning Problems with Total Variation Regularization
نویسندگان
چکیده
We address the problem of optimal scale-dependent parameter learning in total variation image denoising. Such problems are formulated as bilevel optimization instances with denoising lower-level constraints. For problem, we able to derive M-stationarity conditions, after characterizing corresponding Mordukhovich generalized normal cone and verifying suitable constraint qualification conditions. also B-stationarity investigating Lipschitz continuity directional differentiability solution operator. A characterization Bouligand subdifferential mapping, by means a properly defined linear system, is provided well. Based on this characterization, propose two-phase nonsmooth trust-region algorithm for numerical test it computationally two particular experimental settings.
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ژورنال
عنوان ژورنال: Siam Journal on Imaging Sciences
سال: 2022
ISSN: ['1936-4954']
DOI: https://doi.org/10.1137/21m143412x