arXiv (All)
open access
Data relativistic uncertainty framework for low-illumination anime scenery image enhancement
By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and co
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
- Selectivity Estimation for Semantic Filters on Image Data#259595
- Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation#1084232
- Invisible variations: Associations and subtypes of visual imagery vividness extremes#131059
- Transparency, the ultimate camouflage? Background dependency and influence of background lightness#398804
- SGPDFuse: Semantically-Guided Physics-Disentanglement General Multi-Modal Image Fusion#794032
Record · ID 1076792
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