Frozen CLIP Priors for Robust Self-Supervised Poisson Inverse Problems

Self-supervised learning for imaging inverse problems is in-creasingly important in photon-limited settings, where acquiring cleanground truth is impractical and reconstruction must remain stable underdataset and acquisition shifts. This challenge is amplified under Poissonnoise, whose signal-dependent statistics interact with sampling operators(e.g., CFA mosaicing). Meanwhile, foundation vision encoders trained atweb scale offer distortion-invariant, content-related representations thatgeneralize well across domains, suggesting a promising route to buildpriors that transfer beyond the training distribution without expensivefine-tuning. This paper proposes an ADMM-inspired unrolled plug-and-play solver for Poisson inverse problems that decouples a closed-formdata-consistency update from a parameter-efficient prior. The prior isimplemented as a lightweight decoder operating on frozen CLIP RN50dense multi-scale features, adapting foundation representations with lesstrainable parameters. For self-supervision, the method integrates GR2Rmeasurement-domain re-corruption with an Equivariant Imaging regu-larizer via virtual acquisitions. Experiments on Poisson CFA demosaic-ing and deblurring show competitive quality, improved robustness undershifts, and self-supervised performance approaching supervised training.