Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely simulated data, thus inheriting the errors and biases of these underlying sources. Current approaches to finetuning diffusion models rely on a large number of observations with varying forward operators, which can be difficult to collect for many applications, and thus lead to overfitting when the measurement set is small. We propose a method for tuning a prior from only a single observation by combining existing diffusion priors into a single product-of-experts prior and identifying the exponents that maximize the Bayesian evidence. We val...
| Authors | Frederic Wang, Katherine L. Bouman |
|---|---|
| Primary category | cs.CV |
| Published | 22 April 2026 |
| Last updated at source | 24 April 2026 |
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