arXiv Computer Vision
Direct Conditional Transition Sampling for Diffusion Inverse Problems
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
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.
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 Machine Learning
Oct 8
Noise, Denoise, Correct: MCMC Posterior Sampling with Diffusion Priors in Three Steps
The paper introduces Diffusion Waltz, an MCMC algorithm that uses SDEdit-style noising and denoising as a proposal and corrects it with Metropolis-Hastings to achieve exact posterior sampling without evaluating the prior. It also incorporates observations into the proposal via a gradient‑free ensemble Kalman update while maintaining exactness. In experiments on a non‑differentiable Navier‑Stokes initial‑condition recovery problem, Diffusion Waltz outperforms existing baselines across various noise and nonlinearity regimes.
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 Computer Vision
Sep 18
FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants
FlowSGS introduces a flow-based posterior sampling method that combines Split Gibbs Sampling (SGS) with Langevin dynamics for the likelihood step and Stochastic Interpolants (SI) for the prior step. By integrating a pretrained flow model into the prior step via SI's reverse-time SDE and a novel timestep correction, FlowSGS reduces the number of network evaluations compared to plug‑and‑play diffusion samplers. Experiments demonstrate state‑of‑the‑art performance on various inverse problems, including the first flow‑based solution to a nonlinear inverse problem (Fourier phase retrieval).
arXiv Machine Learning
Oct 8
Kinetic Langevin Meets Split Gibbs: Accelerated Posterior Sampling for Imaging Inverse Problems with Diffusion Priors
The paper introduces RED-KLwSGS, a new posterior sampling method for Bayesian imaging inverse problems that combines exact Gaussian updates for the data variable with underdamped (kinetic) Langevin diffusion for the auxiliary variable, driven by a one‑shot denoising score. It achieves the same per‑iteration cost as existing Langevin‑within‑SGS samplers while providing non‑asymptotic Wasserstein‑2 convergence guarantees for strongly log‑concave priors. A further variant, Joint-RED-KLwSGS, applies kinetic Langevin diffusion to both variables, and experiments using denoising diffusion probabilistic models on FFHQ and ImageNet demonstrate faster convergence and high‑quality image reconstruction.