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arXiv Machine Learning By Evan Bell, Jiaming Liu, Yifan Chen, Yu Sun

Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation

Read the original on arXiv Machine Learning →

Generative Translation Priors (GTP) is a Bayesian framework that repurposes diffusion-based image-to-image translation models as cross-modality priors for ill‑posed imaging inverse problems. It incorporates target‑domain measurements via likelihood guidance, steering the translation toward the desired posterior distribution. The authors analyze the resulting posterior dynamics, identify an intrinsic bias from likelihood guidance, and propose a ground‑truth‑free metric to estimate this bias, leading to two discretized GTP algorithms that demonstrate high‑fidelity reconstruction in CT and PET imaging using complementary side information.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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