DART通过时序缺陷掩码增强修复，让老电影修复更精准连贯。
DART: A Degradation-Aware Recurrent Transformer for Archival Film Restoration
- 用递归注意力机制预测并传播软缺陷掩码，显式建模损伤位置与程度。
- 在真实档案数据集上提升无参考感知质量，修复结果更清晰连贯。
- 适合处理含划痕、噪点等复杂退化的老影像修复任务。
历史影像修复面临挑战，因老片常伴有多重退化，如划痕、灰尘、模糊、噪声、闪烁及光度老化，且缺乏干净参考视频。现有方法多隐式处理退化，重建时未明确知晓损伤位置与严重性。本文提出DART——一种面向档案电影修复的退化感知递归变换器。DART通过时间传播软缺陷掩码，指导时序融合，并以损伤位置与严重性为条件调节修复网络，使修复过程显式感知影片瑕疵，而非仅依赖重建损失。在真实档案基准测试中，DART在无参考感知质量上超越先前架构，同时保持模型紧凑高效，生成更清洁、时间一致性更强的结构化损伤修复结果。
原文摘要 · Abstract (English)
Archival film restoration is a challenging problem because historical footage contains compound degradations such as scratches, dust, blur, noise, flicker, and photometric aging, while clean reference videos are unavailable. Existing video restoration methods largely treat these degradations implicitly, reconstructing frames without explicit knowledge of where damage occurs or how severe it is. We propose DART, a degradation-aware recurrent transformer for archival film restoration. DART predicts and propagates a soft defect mask through time, using it to guide temporal fusion and condition the restoration network on both damage location and severity. This makes the restoration process explicitly aware of film artifacts rather than relying only on reconstruction losses. Experiments on real archival benchmarks show that DART improves no-reference perceptual quality over prior restoration architectures while remaining compact and efficient, producing cleaner and more temporally consistent restorations of structured film damage.
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