VICR: Visual In-Context Restoration for Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2606.00704v1
• Date: Sat, 30 May 2026 12:27:16 GMT
• Title: VICR: Visual In-Context Restoration for Real-World Image Super-Resolution
• Abstract summary: We propose Visual In-Context Restoration (VICR), a framework that formulates Real-ISR as image completion.<n>VICR achieves state-of-the-art performance across multiple Real-ISR benchmarks with only 127M trainable parameters.
• Score: 6.277195520534762
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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