ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging
Variational inference (VI) is a powerful method for principled posterior inference for scientific inverse imaging. VI learns the posterior distribution, often with a flow-based network, which can cheaply generate posterior samples upon optimization, and can flexibly incorporate score-based or classic priors. However, its application to large-scale image reconstruction is severely hindered by the poor scalability of the flow-based networks. In this work, we introduce ShuffleFlow, a scalable VI framework to address this challenge. Our method breaks down the problem into three parts: a pixel-unshuffling-based image coordinate sampler, a neural field as feature encoder, and a conditional norm...
| Authors | Tianao Li, Tjitske Starkenburg, Yu Sun et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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