AI RESEARCH
VICR: Visual In-Context Restoration for Real-World Image Super-Resolution
arXiv CS.CV
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ArXi:2606.00704v1 Announce Type: new Real-world image super-resolution (Real-ISR) requires balancing structural fidelity to degraded observations with realistic detail synthesis. However, existing generative Real-ISR methods often rely on entangled conditioning mechanisms, leading to structural drift or semantically inconsistent details. To address this issue, we propose Visual In-Context Restoration (VICR), a Diffusion Transformer (DiT)-based framework that formulates Real-ISR as image completion. Specifically, we