A new research paper proposes a unified framework for integrating various data-driven priors into Bayesian inverse problems. The study demonstrates how diverse priors, including regularization-by-denoising, normalizing flow-based priors, and score-based generative models, can be unified through their score functions. This approach allows for effective integration into a proposed sampling algorithm, with experimental validation in image inpainting and super-resolution tasks. AI
IMPACT This research offers a unified framework for integrating various data-driven priors, potentially improving performance in tasks like image restoration and inverse problem solving.
RANK_REASON The cluster contains an academic paper published on arXiv detailing a new methodology for Bayesian inverse problems.
- Bayesian inverse problems with unknown operators
- convex-ridge regularizers
- Nicolas Dobigeon
- normalizing flow-based priors
- Regularization by Denoising: Clarifications and New Interpretations
- Inpainting
- Single-image super-resolution of brain MR images using overcomplete dictionaries
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