Due to old CRT display technology and limited transmission bandwidth, early film and TV broadcasts commonly used interlaced scanning. This meant each field contained only half of the information. Since modern displays require full frames, this has spurred research into deinterlacing, i.e. restoring the missing information in legacy video content. In this paper, we present a deep-learning-based method for deinterlacing animated and live-action content. Our proposed method supports bidirectional spatio-temporal information propagation across multiple scales to leverage information in both space and time. More specifically, we design a Flow-guided Refinement Block (FRB) which performs feature refinement including alignment, fusion, and rectification. Additionally, our method can process multiple fields simultaneously, reducing per-frame processing time, and potentially enabling real-time processing. Our experimental results demonstrate that our proposed method achieves superior performance compared to existing methods.

翻译：由于早期CRT显示技术的限制以及有限传输带宽，电影和电视广播节目普遍采用隔行扫描方式。这意味着每个场仅包含一半的图像信息。由于现代显示设备需要全帧画面，这推动了去隔行技术的研究，即复原传统视频内容中缺失的信息。本文提出了一种基于深度学习的去隔行方法，适用于动画和实拍内容。该方法支持多尺度双向时空信息传播，充分利用空间和时间维度的信息。具体而言，我们设计了光流引导的细化模块（FRB），该模块包含对齐、融合和校正等特征细化操作。此外，该方法能同时处理多个场，减少单帧处理时间，并有望实现实时处理。实验结果表明，与现有方法相比，本方法具有更优越的性能。