arXiv AI
Jul 22
Provable diffusion-based posterior sampling for linear inverse problems via DDIM
arXiv:2607. 19333v1 Announce Type: cross Abstract: Diffusion-based methods have achieved remarkable empirical success in solving inverse problems.
Direct Conditional Transition Sampling for Diffusion Inverse Problems
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arXiv AI
Jun 30
DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
arXiv:2511. 17038v4 Announce Type: replace Abstract: From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process.
arXiv AI
Sep 4
A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
The paper introduces a Posterior‑Dynamics Framework that leverages pretrained diffusion models as multiscale priors for linear imaging inverse problems such as deblurring, super‑resolution, and inpainting. By constructing a surrogate likelihood centered on the clean image and incorporating diffusion uncertainty, the authors derive continuous posterior dynamics and a tunable Langevin component for adaptive exploration. They prove theoretical guarantees (endpoint consistency, finite‑horizon tracking, weak accuracy) and present the PD‑IMEX sampler, which achieves high‑quality reconstructions with only 100 score evaluations and controllable fidelity‑diversity trade‑offs.
arXiv Machine Learning
Jul 8
Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
arXiv:2602. 11711v2 Announce Type: replace-cross Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems.
arXiv AI
Aug 18
Scale-Consistent Posterior Dynamics for Diffusion Inverse Problems
arXiv:2608. 15144v1 Announce Type: cross Abstract: Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable.
arXiv Machine Learning
Aug 3
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
arXiv:2605. 08328v3 Announce Type: replace Abstract: Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster sampling compared to diffusion models.
arXiv Machine Learning
1d ago
A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
arXiv:2509.19276v2 Announce Type: replace-cross Abstract: Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this t...
arXiv Machine Learning
Jul 7
MACS: Measurement-Aware Consistency Sampling for Inverse Problems
arXiv:2510. 02208v3 Announce Type: replace-cross Abstract: Diffusion models have emerged as powerful generative priors for solving inverse imaging problems.
arXiv AI
Jun 18
Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems
arXiv:2602. 00176v2 Announce Type: replace-cross Abstract: Diffusion posterior sampling solves inverse problems by combining a pretrained diffusion prior with measurement-consistency guidance.
arXiv Machine Learning
Jul 8
A Gibbs posterior sampler for inverse problem based on prior diffusion model
arXiv:2602. 11059v2 Announce Type: replace-cross Abstract: This paper addresses the issue of inversion in cases where (1) the observation system is modeled by a linear transformation and additive error, (2) the problem is ill-posed and regularization relies on a Bayesian strategy, (3)~the prior is modeled by a diffusion process adjusted on an available large set of examples.
arXiv Machine Learning
Jun 16
Exact Posterior Score Estimation for Solving Linear Inverse Problems
arXiv:2606. 17048v1 Announce Type: new Abstract: Diffusion and flow-based models learn powerful data priors by training a denoiser to reverse Gaussian corruption.
arXiv Machine Learning
Jun 2
Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
arXiv:2603. 14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime.