arXiv (All)
open access
Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal
This work studies the joint rain and haze removal problem. In real-life scenarios, rain and haze, two often co-occurring common weather phenomena, can greatly degrade the clarity and quality of the scene images, leading to a performance drop in the visual applications, such as autonomous driving. Ho
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
- Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal#789352
- Tri-DehazeGS: Scene--Medium Decoupled Gaussian Splatting with Transmittance-Aware Optimization#998768
- Denoising of InSAR Time Series Through Spatiotemporal Attentive Convolutional U‐Net#347893
- BiDeMem: Bidirectional Degradation Memory for Explainable Image Restoration#319711
- ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing#1117381
Record · ID 793356
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