{"ID":2922080,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T12:17:29.810469831Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00704","arxiv_id":"2606.00704","title":"VICR: Visual In-Context Restoration for Real-World Image Super-Resolution","abstract":"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 introduce a decoupled visual prior injection mechanism that derives local and global cues from the low-quality (LQ) image: local cues help recover image structures and support high-frequency detail synthesis, while global cues guide overall generation and promote semantic consistency. For ambiguous regions under severe degradation, VICR employs an inference-time agent to refine semantic prompts using visual evidence from the LQ input while keeping model parameters fixed. Experiments show that VICR achieves state-of-the-art performance across multiple Real-ISR benchmarks with only 127M trainable parameters.","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2606.00704","url_pdf":"https://arxiv.org/pdf/2606.00704v1","authors":"[\"Qichang Zhang\",\"Hailong Wang\",\"Baiang Li\",\"Linhao Wang\",\"Rong Fu\",\"Erkang Cheng\",\"Simon James Fong\"]","published":"2026-05-30T12:27:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}