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arXiv · 2209.10192

Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention

Ronglei Ji·A. Murat Tekalp
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Abstract

Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry. This is despite deinterlacing is well-suited for supervised learning from synthetic data since the degradation model is known and fixed. In this paper, we propose a novel multi-field full frame-rate deinterlacing network, which adapts the state-of-the-art superresolution approaches to the deinterlacing task. Our model aligns features from adjacent fields to a reference field (to be deinterlaced) using both deformable convolution residual blocks and self attention. Our extensive experimental results demonstrate that the proposed method provides state-of-the-art deinterlacing results in terms of both numerical and perceptual performance. At the time of writing, our model ranks first in the Full FrameRate LeaderBoard at https://videoprocessing.ai/benchmarks/deinterlacer.html

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Ronglei Ji, A. Murat Tekalp. 2022-09-21. Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention. https://doi.org/10.1109/icip46576.2022.9897353

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