Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.
Ambient diffusion posterior sampling: Solving inverse problems with diffusi on models trained on corrupted data
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Coupled initial noises in diffusion models, with designed dependence but unchanged marginal Gaussians, improve generated image diversity on Stable Diffusion variants while preserving quality and alignment.
Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
citing papers explorer
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Measurement Geometry and Design for Trustworthy Generative Inverse Problems
Introduces a local measurement-manifold compatibility measure that bounds stable reconstruction error under generative priors and motivates fixed and adaptive acquisition rules.
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Couple to Control: Joint Initial Noise Design in Diffusion Models
Coupled initial noises in diffusion models, with designed dependence but unchanged marginal Gaussians, improve generated image diversity on Stable Diffusion variants while preserving quality and alignment.
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Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
Combining diffusion priors as a product-of-experts and optimizing exponents via Bayesian evidence maximization enables prior tuning from one observation in inverse imaging problems.
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A Survey on Diffusion Models for Inverse Problems
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.