ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
Convergence analysis of a proximal stochastic denoising regularization algorithm,
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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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Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction
ML-SPnP accelerates stochastic PnP for SVCT by using MRA approximation spaces where prior-coherence corrections vanish in expectation, yielding comparable quality at reduced runtime.
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Sub-Riemannian Snakes on the Projective Line Bundle with Applications to Segmentation of SEM Images
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.