AI 中文总结
本研究提出模型无关框架ANCHOR，通过物理轨迹估计空间置信场以自适应平衡修复提案与原始观测，在高动态范围视频重建和去雨任务中提升了多种先进修复模型的性能。
AI 中文摘要
视频修复方法利用时间信息恢复退化观测中缺失的信息，但序列内的参考帧可能因物理成像变化、遮挡以及不完善的时间聚合，引入不一致的退化、内容差异或重建误差。现有方法主要聚焦于改进修复网络，而不同空间位置生成输出的可靠性在很大程度上未被探索。本研究提出ANCHOR，一种模型无关的框架，将低质量当前帧作为时间对齐的锚点用于视频修复校正。具体而言，ANCHOR从异构物理轨迹证据中估计空间置信场，并自适应平衡修复提案与原始观测。在高动态范围视频重建和去雨视频上的实验表明，其在多种最先进的修复模型上实现了一致的性能提升，验证了可靠性感知输出校正对视频修复的有效性。
英文摘要
Video restoration methods exploit temporal information to recover information missing from degraded observations. However, reference frames within the sequence may introduce inconsistent degradation, content discrepancy, or reconstruction errors due to physical image-formation variations, occlusion, and imperfect temporal aggregation. Existing approaches mainly focus on improving restoration networks, while the reliability of the generated outputs at different spatial locations remains largely unexplored. In this work, we propose ANCHOR, a model-agnostic framework that revisits the low-quality current frame as a temporally aligned anchor for video restoration correction. Specifically, ANCHOR estimates a spatial trust field from heterogeneous physical-trace evidence and adaptively balances the restoration proposal with the original observation. Experiments on High Dynamic Range video reconstruction and video deraining demonstrate consistent improvements across various state-of-the-art restoration models, validating the effectiveness of reliability-aware output correction for video restoration.
Comments9 pages, 6 figures
