Frozen CLIP Priors for Robust Self-Supervised Poisson Inverse Problems
Self-supervised learning for imaging inverse problems is increasingly important in photon-limited settings, where acquiring clean ground truth is impractical and reconstruction must remain stable under dataset and acquisition shifts. This challenge is amplified under Poisson noise, whose signal-dependent statistics interact with sampling operators (e.g., CFA mosaicing). Meanwhile, foundation vision encoders trained at web scale offer distortion-invariant, content-related representations that generalize well across domains, suggesting a promising route to build priors that transfer beyond the training distribution without expensive fine-tuning. This paper proposes an ADMM-inspired unrolled...
| Authors | Laura C. Diaz-Delgado, Emmanuel Martinez, Henry Arguello |
|---|---|
| Primary category | eess.IV |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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