Multi-frame Joint Enhancement for Early Interlaced Videos
This addresses visual quality issues in early videos for restoration applications, but it is incremental as it builds on existing deinterlacing methods by incorporating temporal data.
The paper tackles the problem of deinterlacing early interlaced videos with complex artifacts by proposing a multi-frame joint enhancement network that leverages temporal information, and it demonstrates recovery of high-quality results on synthetic and real-world datasets.
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditional methods mainly focus on simple interlacing mechanism, and cannot deal with the complex artifacts in real-world early videos. Recent interlaced video reconstruction deep deinterlacing models only focus on single frame, while neglecting important temporal information. Therefore, this paper proposes a multiframe deinterlacing network joint enhancement network for early interlaced videos that consists of three modules, i.e., spatial vertical interpolation module, temporal alignment and fusion module, and final refinement module. The proposed method can effectively remove the complex artifacts in early videos by using temporal redundancy of multi-fields. Experimental results demonstrate that the proposed method can recover high quality results for both synthetic dataset and real-world early interlaced videos.