Multi-frame Joint Enhancement for Early Interlaced Videos
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.
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Datasets
Vimeo90K
MSU Deinterlacer Benchmark
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Video Deinterlacing | MSU Deinterlacer Benchmark | MFDIN | PSNR | 39.803 | # 12 | |
| SSIM | 0.961 | # 11 | ||||
| FPS on CPU | 1.6 | # 18 | ||||
| Subjective | 0.963 | # 2 | ||||
| VMAF | 94.38 | # 8 | ||||
| Video Deinterlacing | MSU Deinterlacer Benchmark | MFDIN (L) | PSNR | 43.884 | # 1 | |
| SSIM | 0.979 | # 1 | ||||
| FPS on CPU | 1.6 | # 18 | ||||
| Subjective | 1.054 | # 1 | ||||
| VMAF | 97.30 | # 1 |
