arXiv (All)
open access
Learning Where and What to Restore for Composite Image Restoration
Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different from the single-degradation setting assumed by most existing all-in-one methods. These methods typically apply uniform spatial computation and single-label tas
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
- MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration#974750
- TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback#1105728
- GraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration#1122602
- UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration#998708
- FlowSteer: Conditioning Flow Field for Consistent Image Restoration#1050792
Record · ID 1117221
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