Revitalizing Legacy Video Content: Deinterlacing with Bidirectional Information Propagation
This addresses the practical need to convert old interlaced video formats for contemporary viewing, representing an incremental improvement in a specific technical domain.
The paper tackles the problem of deinterlacing legacy video content for modern displays by proposing a deep-learning method that uses bidirectional spatio-temporal information propagation, achieving superior performance compared to existing methods.
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.