Analog Video

Analog video restoration and analysis are active research areas focusing on mitigating degradation from aging media and interference. Current efforts leverage deep learning models, including convolutional neural networks (CNNs), transformers, and Swin-UNet architectures, to address issues like tape mistracking, artifact removal (e.g., scratches, dust), and electromagnetic interference. These techniques often utilize multi-frame approaches and reference images to improve restoration accuracy, with a growing emphasis on creating realistic synthetic datasets for training and evaluation. This research is crucial for preserving historical video archives and improving the reliability of analog video systems in various applications.