The paper introduces MS-Flow, a method that represents a flow-based generative model’s trajectory as a sequence of intermediate latent states instead of a single initial code. By enforcing local flow dynamics and coupling trajectory segments with matching penalties, the approach alternates between updating latent states and ensuring consistency with observed data. This strategy reduces memory usage and improves reconstruction quality on tasks such as image inpainting, super‑resolution, and computed tomography.
By Alexander Denker, Zeljko Kereta, Carola-Bibiane Sch\"onlieb, Moshe Eliasof
The paper introduces FLINT, a two‑stage framework for flow‑matching inverse problems that couples proximal initialization with bounded stochasticity. It shows that a moderately optimized proximal source initialization can reach a performance plateau, but this advantage is lost if stochasticity accumulates too much during guided sampling. FLINT first establishes a high‑fidelity starting point via a manifold‑constrained proximal objective, then performs guided sampling under a bounded stochasticity schedule, achieving state‑of‑the‑art perceptual quality and competitive distortion metrics with superior computational efficiency.
By Junseo Bang, Daewon Choi, Dong Ju Mun, Se Young Chun
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).
By Tianao Li, Xinhui Qian, Emma Alexander
arXiv:2601. 23231v2 Announce Type: replace-cross Abstract: Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains challenging.
By George Webber, Alexander Denker, Riccardo Barbano, Andrew J Reader
FlowSGS introduces a flow-based posterior sampling method that combines Split Gibbs Sampling with annealed coupling and Stochastic Interpolants to improve flow priors for inverse imaging. The approach includes a novel timestep correction and accelerated warm-up using one-step flow models, resulting in faster and more accurate performance across various imaging tasks. It also demonstrates the first application of flow-based samplers to a nonlinear inverse problem, namely Fourier phase retrieval, and extends one-step flow models to principled Bayesian inverse imaging.
By Tianao Li, Xinhui Qian, Emma Alexander
arXiv:2609.37529v1 Announce Type: cross
Abstract: Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-n...
By Alexandre Lagier, Valentine Tosel, Anne Gagneux, Mathurin Massias, S\'egol\`ene Martin