Papers › Real-time Deep Video Deinterlacing
Real-time Deep Video Deinterlacing
Haichao Zhu, Xueting Liu, Xiangyu Mao, Tien-Tsin Wong
Interlacing is a widely used technique, for television broadcast and video recording, to double the perceived frame rate without increasing the bandwidth. But it presents annoying visual artifacts, such as flickering and silhouette "serration," during the playback. Existing state-of-the-art deinterlacing methods either ignore the temporal information to provide real-time performance but lower visual quality, or estimate the motion for better deinterlacing but with a trade-off of higher computational cost. In this paper, we present the first and novel deep convolutional neural networks (DCNNs) based method to deinterlace with high visual quality and real-time performance. Unlike existing models for super-resolution problems which relies on the translation-invariant assumption, our proposed DCNN model utilizes the temporal information from both the odd and even half frames to reconstruct only the missing scanlines, and retains the given odd and even scanlines for producing the full deinterlaced frames. By further introducing a layer-sharable architecture, our system can achieve real-time performance on a single GPU. Experiments shows that our method outperforms all existing methods, in terms of reconstruction accuracy and computational performance.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Deinterlacing | MSU Deinterlacer Benchmark | Real-time Deep Video Deinterlacing | FPS on CPU | 0.3 | #11 of 31 | Archive leaderboard | report |
| Video Deinterlacing | MSU Deinterlacer Benchmark | Real-time Deep Video Deinterlacing | PSNR | 38.374 | #11 of 31 | Archive leaderboard | report |
| Video Deinterlacing | MSU Deinterlacer Benchmark | Real-time Deep Video Deinterlacing | SSIM | 0.957 | #11 of 31 | Archive leaderboard | report |
| Video Deinterlacing | MSU Deinterlacer Benchmark | Real-time Deep Video Deinterlacing | Subjective | 0.543 | #11 of 31 | Archive leaderboard | report |
| Video Deinterlacing | MSU Deinterlacer Benchmark | Real-time Deep Video Deinterlacing | VMAF | 93.28 | #11 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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