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.
Deepinverse: A python package for solving imaging inverse problems with deep learning
2 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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Pith papers citing it
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years
2026 2verdicts
UNVERDICTED 2representative citing papers
A self-supervised conformal prediction method with equivariant bootstrapping enables uncertainty quantification for ill-posed imaging inverse problems such as weak lensing mass mapping without requiring ground truth calibration data.
citing papers explorer
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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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Self-Supervised Conformal Prediction with Equivariant Bootstrapping for Image Uncertainty Quantification
A self-supervised conformal prediction method with equivariant bootstrapping enables uncertainty quantification for ill-posed imaging inverse problems such as weak lensing mass mapping without requiring ground truth calibration data.