Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.
Journal of Mathematical Imag- ing and Vision65, 209–239 (2023) https://doi
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A new sub-Riemannian snake model on the projective line bundle uses a symmetric cusp-free pseudo-distance with triangle inequality properties and connected-component costs to enable efficient robust segmentation of overlapping objects in SEM images.
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A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems
Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.