Analog magnetic tapes have been the main video data storage device for several decades. Videos stored on analog videotapes exhibit unique degradation patterns caused by tape aging and reader device malfunctioning that are different from those observed in film and digital video restoration tasks. In this work, we present a reference-based approach for the resToration of digitized Analog videotaPEs (TAPE). We leverage CLIP for zero-shot artifact detection to identify the cleanest frames of each video through textual prompts describing different artifacts. Then, we select the clean frames most similar to the input ones and employ them as references. We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks. MRSFF blocks rely on cross-attention and attention pooling to take advantage of the most useful parts of each reference frame. To address the absence of ground truth in real-world videos, we create a synthetic dataset of videos exhibiting artifacts that closely resemble those commonly found in analog videotapes. Both quantitative and qualitative experiments show the effectiveness of our approach compared to other state-of-the-art methods. The code, the model, and the synthetic dataset are publicly available at https://github.com/miccunifi/TAPE.

翻译：模拟磁带曾作为主要视频数据存储设备长达数十年。存储于模拟磁带中的视频会因磁带老化及读取设备故障而产生独特的退化模式，这与胶片及数字视频修复任务中观察到的退化模式存在差异。本文提出一种基于参考的数字化模拟磁带修复方法（TAPE）。我们利用CLIP实现零样本伪影检测，通过描述不同伪影的文本提示识别每个视频中的最清晰帧。随后，选取与输入帧相似度最高的清晰帧作为参考。我们设计了一种基于Transformer的Swin-UNet网络，通过所提出的多参考空间特征融合（MRSFF）模块，同时利用相邻帧与参考帧。MRSFF模块依赖交叉注意力与注意力池化机制，有效提取每个参考帧中最具价值的部分。针对真实视频缺乏真值标注的问题，我们构建了一个合成视频数据集，其伪影特征与模拟磁带常见退化现象高度吻合。定量与定性实验均表明，与当前最优方法相比，本方法具有显著优势。相关代码、模型及合成数据集已公开于https://github.com/miccunifi/TAPE。