Binarized High-Efficiency RAW Video Restoration and Beyond
RAW video restoration is fundamental to high-quality low-level perception and serves as the basis for a wide range of downstream vision applications. While binary neural networks (BNNs) enable efficient lightweight deployment for image enhancement, their deficiencies in modeling temporal coherence and activation value distributions hinder their effectiveness when applied to video scenarios. In this paper, we propose BinRVR, a binarized RAW video restoration framework that reduces computation and parameters by approximately 96% while incurring only about 4% performance degradation. Specifically
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “Binarized High-Efficiency RAW Video Restoration and Beyond” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Tianyu Zhu →
“Binarized High-Efficiency RAW Video Restoration and Beyond”
- LinkedLinked via arxiv author · 85%Ying Fu →
“Binarized High-Efficiency RAW Video Restoration and Beyond”
- LinkedLinked via arxiv author · 85%Hesong Li →
“Binarized High-Efficiency RAW Video Restoration and Beyond”
- LinkedLinked via arxiv author · 85%Gengchen Zhang →
“Binarized High-Efficiency RAW Video Restoration and Beyond”
- LinkedLinked via arxiv author · 85%Xin Yuan →
“Binarized High-Efficiency RAW Video Restoration and Beyond”
- LinkedLinked via arxiv author · 85%Yulun Zhang →
“Binarized High-Efficiency RAW Video Restoration and Beyond”