用拍摄元数据和序列选择机制，提升暗光视频去噪效果。
DarkVRAI: Capture-Condition Conditioning and Burst-Order Selective Scan for Low-light RAW Video Denoising
- 引入拍摄元数据作为条件，指导去噪对齐
- 通过有序扫描建模长时序依赖，显著降噪
- 适合图像/视频去噪研究者与算法工程师
暗光RAW视频去噪因高增益和短曝光导致信号严重退化，且受限于帧率要求，极具挑战。本文提出DarkVRAI框架，在AIM 2025暗光RAW视频去噪挑战赛中获得第一名。方法创新性地将图像去噪中的条件建模应用于视频去噪，显式利用拍摄元数据引导对齐与去噪过程；同时设计了突发顺序选择扫描（BOSS）机制，有效建模视频序列中的长程时序依赖。两者协同工作，在严谨真实的基准数据集上实现当前最优性能，为暗光视频去噪树立新标准。
原文摘要 · Abstract (English)
Low-light RAW video denoising is a fundamentally challenging task due to severe signal degradation caused by high sensor gain and short exposure times, which are inherently limited by video frame rate requirements. To address this, we propose DarkVRAI, a novel framework that achieved first place in the AIM 2025 Low-light RAW Video Denoising Challenge. Our method introduces two primary contributions: (1) a successful application of a conditioning scheme for image denoising, which explicitly leverages capture metadata, to video denoising to guide the alignment and denoising processes, and (2) a Burst-Order Selective Scan (BOSS) mechanism that effectively models long-range temporal dependencies within the noisy video sequence. By synergistically combining these components, DarkVRAI demonstrates state-of-the-art performance on a rigorous and realistic benchmark dataset, setting a new standard for low-light video denoising.
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