2025-01-192022978-1-6654-9620-91522-488010.1109/ICIP46576.2022.98973532-s2.0-85146709598https://doi.org/10.1109/ICIP46576.2022.9897353https://hdl.handle.net/20.500.14288/26303Although 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.htmlComputer scienceElectrical and electronics engineeringConference proceeding1058109501002N/A50738