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Arxiv
2d
167
Image Credit: Arxiv
Learning few-step posterior samplers by unfolding and distillation of diffusion models
- Diffusion models (DMs) are powerful image priors in Bayesian computational imaging.
- Two primary strategies for leveraging DMs are Plug-and-Play methods and specialized conditional DMs.
- A novel framework integrating deep unfolding and model distillation transforms a DM image prior into a few-step conditional model for posterior sampling.
- The approach includes unfolding a Markov chain Monte Carlo algorithm and achieves excellent accuracy, efficiency, and flexibility in adapting to variations in the forward model.
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