FlowSGS: Improving Flow Priors for Inverse Imaging with Stochastic Interpolants
Flow matching has emerged as the state-of-the-art generative model and has been used for plug-and-play (PnP) priors to solve inverse problems in computational imaging. However, existing flow-based inverse solvers make simplifying approximations in posterior sampling and/or assume linear forward models. In contrast, principled diffusion-based methods exist but are computationally expensive. To circumvent these problems, we introduce FlowSGS, a flow-based posterior sampling method using Split Gibbs Sampling (SGS) with annealed coupling, and make flow-specific interventions to the prior sampling and annealing steps, theoretically grounded in the Stochastic Interpolants (SI) framework. Using ...
| Authors | Tianao Li, Xinhui Qian, Emma Alexander |
|---|---|
| Primary category | cs.CV |
| Published | 17 September 2026 |
| Last updated at source | 8 October 2026 |
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