UOTIP learns an unbalanced optimal transport map from noisy to clean distributions for unpaired inverse problems, incorporating a likelihood cost and proving existence/uniqueness via quadratic cost satisfying the twist condition.
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Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon
11 Pith papers cite this work. Polarity classification is still indexing.
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DiLO turns diffusion sampling into deterministic latent optimization to satisfy the manifold consistency requirement for neural operators in inverse problem solving.
DiME estimates model evidence for diffusion priors by integrating time-marginals from posterior sampling, enabling efficient prior selection and misfit diagnosis in ill-posed inverse problems.
Anisotropic SPDEs preserve geometric data structure over longer timescales in score-based generative modeling, yielding better image quality than standard SDE baselines and flow matching in unconditional and conditional tasks.
GDM reformulates 3D conditional medical image generation as attractive-repulsive drifting with multi-level feature banks to balance distribution plausibility, patient fidelity, and one-step inference, outperforming GANs, flows, and SDEs on MRI-to-CT and sparse CT tasks.
A wavelet diffusion model combined with diffusion posterior sampling enables joint 3D activity-attenuation reconstruction in PET from emission data alone, outperforming MLAA on simulated TOF data.
3DGR-CT adapts 3D Gaussian splatting with FBP-guided initialization and differentiable CT projection for sparse-view reconstruction, claiming better accuracy and speed than prior methods.
Piecewise guidance in diffusion posterior sampling cuts inference time 23-25% on inpainting and super-resolution with negligible PSNR/SSIM loss while handling measurement noise.
A dual ascent optimization framework is introduced for MAP estimation with diffusion priors, claimed to outperform prior methods on image restoration in quality, noise robustness, speed, and data fidelity.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
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UOTIP: Unbalanced Optimal Transport Map for Unpaired Inverse Problems
UOTIP learns an unbalanced optimal transport map from noisy to clean distributions for unpaired inverse problems, incorporating a likelihood cost and proving existence/uniqueness via quadratic cost satisfying the twist condition.
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DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems
DiLO turns diffusion sampling into deterministic latent optimization to satisfy the manifold consistency requirement for neural operators in inverse problem solving.
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Sample-efficient evidence estimation of score based priors for model selection
DiME estimates model evidence for diffusion priors by integrating time-marginals from posterior sampling, enabling efficient prior selection and misfit diagnosis in ill-posed inverse problems.
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Score-Based Generative Modeling through Anisotropic Stochastic Partial Differential Equations
Anisotropic SPDEs preserve geometric data structure over longer timescales in score-based generative modeling, yielding better image quality than standard SDE baselines and flow matching in unconditional and conditional tasks.
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Generative Drifting for Conditional Medical Image Generation
GDM reformulates 3D conditional medical image generation as attractive-repulsive drifting with multi-level feature banks to balance distribution plausibility, patient fidelity, and one-step inference, outperforming GANs, flows, and SDEs on MRI-to-CT and sparse CT tasks.
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Joint Reconstruction of Activity and Attenuation in PET by Diffusion Posterior Sampling in Wavelet Coefficient Space
A wavelet diffusion model combined with diffusion posterior sampling enables joint 3D activity-attenuation reconstruction in PET from emission data alone, outperforming MLAA on simulated TOF data.
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3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation
3DGR-CT adapts 3D Gaussian splatting with FBP-guided initialization and differentiable CT projection for sparse-view reconstruction, claiming better accuracy and speed than prior methods.
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Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
Piecewise guidance in diffusion posterior sampling cuts inference time 23-25% on inpainting and super-resolution with negligible PSNR/SSIM loss while handling measurement noise.
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Dual Ascent Diffusion for Inverse Problems
A dual ascent optimization framework is introduced for MAP estimation with diffusion priors, claimed to outperform prior methods on image restoration in quality, noise robustness, speed, and data fidelity.
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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.
- Variance Reduction for Expectations with Diffusion Teachers