Research·arXiv cs.LG·16h ago

Domain shift breaks denoiser convergence in plug-and-play image reconstruction

Researchers tackle a fundamental challenge in plug-and-play image reconstruction: when denoisers trained on one domain are deployed on another, convergence guarantees collapse. This work formalizes 'proximal mismatch' as the gap between a deployed denoiser and its target-domain reference, showing how domain shift degrades each denoising step into an inexact proximal operation. The finding matters for practitioners using pre-trained denoisers across imaging tasks (medical, computational photography, inverse problems) where retraining is impractical. Understanding this mismatch opens paths to either adapting denoisers or redesigning algorithms robust to domain drift, directly improving reliability of neural-prior-based reconstruction pipelines.

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Explainer

The paper formalizes why pre-trained denoisers fail across domains as a mathematical property (inexact proximal operations), not just empirical degradation. This shifts the problem from 'denoisers perform worse' to 'the algorithm's convergence proof no longer holds', which opens different solution paths.

This connects directly to the medical imaging work on prompt tuning of vision foundation models (story 3) and the multimodal contrastive learning framework (story 4). Both tackle the tension between using frozen, pre-trained components and adapting them to new domains without full retraining. Where those papers propose parameter-efficient adaptation strategies, this work provides the theoretical foundation for why such adaptation is necessary: domain mismatch isn't just a performance leak, it's a correctness problem. The same pattern appears in AlphaWiSE (story 6), which addresses catastrophic forgetting in multimodal systems by interpolating between checkpoints rather than retraining. Here, the stakes are higher because the algorithm's guarantees collapse, not just performance.

If follow-up work demonstrates that simple domain adaptation of the denoiser (fine-tuning on unlabeled target-domain data) recovers convergence guarantees without full retraining, that validates the practical pathway. If instead the field moves toward algorithm redesign that's robust to proximal mismatch, that signals the adaptation route is harder than expected and practitioners should expect longer development cycles for cross-domain imaging pipelines.

Coverage we drew on

Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening · arXiv cs.LG

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MentionsPlug-and-play proximal gradient descent · Image reconstruction · Domain adaptation · Denoiser

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.