arXiv (All)
open access
HarnessIR: Harnessing Multimodal Foundation Models for Universal Real-World Image Restoration
Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic methods usually model real-world image restoration (Real-IR) as a sequential tool calling problem over task-specific single-degradation restoration
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration#998708
- BiDeMem: Bidirectional Degradation Memory for Explainable Image Restoration#319711
- Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal#793356
- Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal#789352
- Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning#1064779
Record · ID 1234500
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive