用当前帧做参考，修正视频修复结果的可靠性问题。
Revisiting the Current Frame: Physical-Trace-Guided Network Output Correction for Video Restoration
- 基于物理成像痕迹估计可信度图，动态调整修复结果
- 在高动态范围重建和去雨任务中提升多个主流模型性能
- 适合需要高精度视频修复的应用场景
视频修复方法利用时序信息恢复退化观测中缺失的信息。然而，序列中的参考帧可能因物理成像差异、遮挡或时序聚合不完善而引入不一致退化、内容偏差或重建误差。现有方法多聚焦于改进修复网络，但对不同空间位置生成结果的可靠性仍缺乏探索。本文提出 ANCHOR，一种与模型无关的框架，重新审视低质量当前帧作为时序对齐的锚点，用于视频修复结果校正。具体而言，ANCHOR 从异构物理痕迹证据中估计空间可信度场，并自适应地平衡修复建议与原始观测。在高动态范围视频重建和视频去雨任务上的实验表明，该方法在多种先进修复模型上均实现稳定提升，验证了可靠性感知输出校正的有效性。
原文摘要 · Abstract (English)
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.
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