发表机构
Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对档案影片修复难题，现有方法隐式处理退化问题。本文提出DART，通过预测和传播软缺陷掩码，依据损伤位置和严重程度指导修复网络，实验证明其能提升无参考感知质量，高效修复影片损伤。
AI 中文摘要
档案影片修复是一个具有挑战性的问题，因为历史影片存在划痕、灰尘、模糊、噪声、闪烁和光度老化等复合退化，且没有干净的参考视频。现有视频修复方法大多隐式处理这些退化。我们提出了DART，一种用于档案影片修复的退化感知循环Transformer。DART通过时间预测和传播软缺陷掩码，以指导时间融合并根据损伤位置和严重程度调整修复网络。实验表明，DART在保持紧凑高效的同时，能提高无参考感知质量，产生更清晰、时间上更一致的结构化影片损伤修复效果。
英文摘要
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.
CommentsAccepted to ACCV 2026. 14 pages + references, 6 figures, 4 tables. Project page: https://www.mikjas.com/dart Code: https://github.com/TytanMikJas/DART-Degradation-Aware-Recurrent-Transformer
