Generative Translation Priors: Bayesian Imaging with Cross-Modality Image Translation
The ability to leverage images from co-available modalities to inform target-domain reconstruction is highly desirable in imaging algorithms. In this work, we introduce Generative Translation Priors (GTP)--a Bayesian framework that transforms diffusion-based image-to-image translation models into cross-modality image priors for ill-posed imaging inverse problems. GTP incorporates target-domain measurements through likelihood guidance, steering the translation process toward the desired posterior distribution. The framework is grounded in a theoretical analysis of the resulting posterior dynamics, which reveals an intrinsic bias introduced by likelihood guidance. We further characterize th...
| Authors | Evan Bell, Jiaming Liu, Yifan Chen et al. |
|---|---|
| Primary category | eess.IV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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