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September 29, 2025Open Access

FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems

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FlowDPS effectively achieves posterior sampling for inverse problems by integrating flow models.
The method decomposes the flow ODE into clean image and noise estimation components for enhanced accuracy.
By leveraging a latent flow model with a transformer architecture, FlowDPS integrates seamlessly into existing frameworks.
Compared to state-of-the-art methods, FlowDPS showcased superior performance across four linear inverse problems.

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This method demonstrates improved performance in inverse problem solving using generative modeling and flow techniques.

Kim et al. (2025) studied this question.

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