Restoration of Analog Videos Using Swin-UNet
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Abstract (arXiv)
In this paper, we present a system to restore analog videos of historical archives. These videos often contain severe visual degradation due to the deterioration of their tape supports that require costly and slow manual interventions to recover the original content. The proposed system uses a multi-frame approach and is able to deal with severe tape mistracking, which results in completely scrambled frames. Tests on real-world videos from a major historical video archive show the effectiveness of our demo system. The code and the pre-trained model are publicly available at https://github.com/miccunifi/analog-video-restoration.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 7 Nov 2023.
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8 of 13 references.
References · 8
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
- 2016Generating Images with Perceptual Similarity Metrics based on Deep Networkscited by 221
- 2022Bringing Old Films Back to Lifecited by 31
- 2020DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancementcited by 10
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