arXiv (All)
open access
FilterFusion: Prior-Guided Adaptive Filter Fusion for Efficient Video Denoising
Real-world video denoising for communication systems must handle mixed degradations such as sensor noise, compression artifacts, and motion-induced inconsistency while remaining deployable under tight latency budgets. Purely deep models often achieve high accuracy but come with significant computati
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
- PDMD: Projected Distribution Matching Distillation for Video Diffusion Models#1130413
- PDMD: Projected Distribution Matching Distillation for Video Diffusion Models#1108739
- Dynamic Video Generation: Shaping Video Generation Across Time and Space#1357168
- ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion#1296632
- Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout#679609
Record · ID 1356841
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