Query-to-Competence: Usage-Time Continual Adaptation for Real-World Image Restoration

Real-world image restoration must handle compound and continually changing degradations. We consider a deployment setting that combines an updateable self-hosted model, referred to as the Student, with a hosted image-editing service, referred to as the Teacher. In a service receiving many complex restoration requests, the Teacher can resolve difficult cases, but repeated calls incur provider cost. Our goal is to progressively reduce hosted-service dependence by turning quality-triggered usage-time Teacher calls into reusable Student competence. We propose Query-to-Competence (Q2C), a usage-time adaptive restoration system for this purpose. Q2C constructs a Structured Restoration Brief (SRB) describing the degradations, restoration intent, and content-preservation constraints. The Student processes each request first, and the Teacher is queried only when the Student output fails a fixed quality criterion. Curated Teacher outputs are accumulated for Replay-Distill Adaptation (RDA), while Guarded Student Promotion (GSP) determines whether an updated candidate enters deployment. Across seven actual usage sessions, the deployed Student handles an increasing fraction of requests without Teacher fallback. On the fixed SDCR-200 evaluation set, Student-only coverage increases from 24.0% to 81.5%, showing that selected Teacher feedback can be retained as reusable Student competence.