Pengfei Jin
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The paper introduces Robust Prior Update (RPU), a module that improves the faithfulness of diffusion-based inverse solvers by stabilizing the prior update step, thereby reducing measurement-conditioned hallucination.
The paper proposes a measurement-geometry framework to quantify how well fixed measurement operators can distinguish between images generated by a prior, thereby guiding the design of more trustworthy and informative acquisition protocols.
Papers
Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates
Pengfei Jin, Yiqi Tian, Kailong Fan, Bingjie Qi +1 more
The paper introduces Robust Prior Update (RPU), a module that improves the faithfulness of diffusion-based inverse solvers by stabilizing the prior update step, thereby reducing measurement-conditione…