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.
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.
Related stories
arXiv Machine Learning
Jun 2
Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates
arXiv:2606. 02331v1 Announce Type: cross Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supported by the measurement.
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
1d ago
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
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 Machine Learning
Jun 3
RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction
arXiv:2606. 03074v1 Announce Type: new Abstract: Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps must be refreshed repeatedly.
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 4
Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction
arXiv:2602. 23214v2 Announce Type: replace-cross Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors.
arXiv Machine Learning
Jul 7
Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models
arXiv:2607. 03517v1 Announce Type: new Abstract: Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge from the clean signal directly to the degraded observation, rather than to pure noise.
arXiv Machine Learning
Jun 29
StableMotion: One-Step Motion Estimation with Diffusion Prior
arXiv:2505. 06668v2 Announce Type: replace-cross Abstract: We present StableMotion, a novel framework that leverages geometric and content priors from pretrained large-scale image diffusion models for motion estimation in single-image rectification tasks such as Stitched Image Rectangling (SIR) and Rolling Shutter Correction (RSC).
arXiv Machine Learning
Jun 3
Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
arXiv:2601. 22443v2 Announce Type: replace Abstract: Can a diffusion model trained on bedrooms recover human faces?
arXiv AI
Jul 14
FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion
arXiv:2607. 10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation.